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0.1 Exploratory factor analysis: 7 factors as the number of variables in the study design

0.2 Read in data

all <- read.csv("../02-descriptive_data/merged_filtered_imputedMedian_likertNumber.csv")
rownames(all) <- all$Resp.ID

Seven, is the number of factors that would be present according to the study design. Using very relaxed cutoff of 0.2 to get rid of not important variables in each factor.

0.2.1 Likert variables

1 Final Factanal correcting for degree and Context and not for L2

When correcting for context what we are doing is that we are removing the context mean from every context

# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
usable_data <- all[,c(usable_items,"Context","degree")]
usable_data$degree_binary <- ifelse(usable_data$degree %in% c("HUM.SCI","SCI"), "SCI",
                                    ifelse(usable_data$degree %in% "LA","LA","HUM"))
dat_onlyItems <- usable_data[,usable_items]
# get residuals after regressing for context
get_residuals <- function(item,pred1,pred2){
  mod <- lm(item ~ pred1 + pred2)
  return(mod$residuals)
}
applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals,
                     pred1=usable_data$Context,pred2=usable_data$degree_binary)

Compare correlation matrix before and after correcting

before <- cor(as.matrix(dat_onlyItems))
after <- cor(applygetRes)
dif <- before - after
hist(dif)

########################
### added
# before <- data.frame(dat_onlyItems)
# after <- data.frame(applygetRes)
# 
# cov <- cor(after,method = "pearson",use="pairwise.complete.obs")
# 
# row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
# row_infos$Variables <- as.character(row_infos$Variables)
# rownames(row_infos) <- rownames(cov)
# row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
# row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
# 
# ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
# ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
# 
# pheatmap(cov, main = "All",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
# ,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
# ### added
# 
# after$Context <- usable_data$Context[match(rownames(after),rownames(usable_data))]
# before$Context <- usable_data$Context[match(rownames(before),rownames(usable_data))]
# 
# ggplot(after,aes(x=fail.ought1,fill=Context)) + geom_histogram() + facet_wrap(~Context)
# plot(after$fail.ought1,before$fail.ought1)
# ggplot(before,aes(x=speaking.prof1,fill=Context)) + geom_histogram() + facet_wrap(~Context)
# ggplot(before,aes(x=fail.ought1,fill=Context)) + geom_histogram() + facet_wrap(~Context)
# Factanal 
# From a statisticak point of view 
fap <- fa.parallel(applygetRes)
Parallel analysis suggests that the number of factors =  6  and the number of components =  4 

fact <- 6
loading_cutoff <- 0.2
fa_basic <- fa(applygetRes,fact)
Loading required namespace: GPArotation
# analyse residuals vs initial
#fa_basic <- fa(applygetRes, fact,scores="regression")
#fac <- as.matrix(dat_onlyItems) %*% loadings(fa_basic,cutoff = 0.3)
# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic
a2 <- melt(a1,id.vars=c("D"))
a2$inv <- ifelse(a2$value < 0 ,"neg","pos")
a2$value[abs(a2$value) < loading_cutoff] <- 0
a2 <- a2[a2$value!=0,]
a2 <- a2 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
ggplot(a2)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")

# Factors one by one
ggplot(subset(a2,variable %in% "F1"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F1")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F2"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F2") + labs(y="Items")

ggplot(subset(a2,variable %in% "F3"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F3")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F4"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F4")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F5"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F5")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F6"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F6")+ labs(y="Items")

1.1 Chronbach alpha

f1 <- unique(a2$D[a2$variable %in% "F1"])
f2 <- unique(a2$D[a2$variable %in% "F2"])
f3 <- unique(a2$D[a2$variable %in% "F3"])
f4 <- unique(a2$D[a2$variable %in% "F4"])
f5 <- unique(a2$D[a2$variable %in% "F5"])
f6 <- unique(a2$D[a2$variable %in% "F6"])
psych::alpha(applygetRes[,colnames(applygetRes) %in% f1])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f1])

 

 lower alpha upper     95% confidence boundaries
0.8 0.83 0.86 

 Reliability if an item is dropped:

 Item statistics 
psych::alpha(applygetRes[,colnames(applygetRes) %in% f2])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f2])

 

 lower alpha upper     95% confidence boundaries
0.73 0.77 0.8 

 Reliability if an item is dropped:

 Item statistics 
psych::alpha(applygetRes[,colnames(applygetRes) %in% f3])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f3])

 

 lower alpha upper     95% confidence boundaries
0.67 0.72 0.77 

 Reliability if an item is dropped:

 Item statistics 
psych::alpha(applygetRes[,colnames(applygetRes) %in% f4])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f4])

 

 lower alpha upper     95% confidence boundaries
0.69 0.73 0.78 

 Reliability if an item is dropped:

 Item statistics 
psych::alpha(applygetRes[,colnames(applygetRes) %in% f5])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f5])

 

 lower alpha upper     95% confidence boundaries
0.7 0.74 0.78 

 Reliability if an item is dropped:

 Item statistics 
psych::alpha(applygetRes[,colnames(applygetRes) %in% f6])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f6])

 

 lower alpha upper     95% confidence boundaries
0.55 0.62 0.68 

 Reliability if an item is dropped:

 Item statistics 
# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
kable(loading_fact_reduced)

F1 F2 F3 F4 F5 F6
converse.id1 0.44
dream.id1 0.29 0.21 0.21 -0.27
usewell.id1 0.22 0.24
whenever.id1 0.23 0.2 0.34
consider.ought1 0.56
people.ought1 0.53
expect.ought1 0.82
fail.ought1 0.62
enjoy.intr1 0.74
life.intr1 0.55
exciting.intr1 0.21 0.38
challenge.intr1 0.46
job.instru1 0.78
knowledge.instru1 0.22 0.37
career.instru1 0.71
money.instru1 0.53
time.integr1 0.64
becomelike.integr1 0.37 0.27
meeting.integr1 0.56
affinity.integr1 0.68
improve.prof1 0.64
speaking.prof1 0.74
reading.prof1 0.68
written.prof1 0.78
listening.prof1 0.83
citizen.post1 0.56
interact.post1 0.21
overseas.post1 0.29 0.26
globalaccess.post1 0.3 0.33

# predict values per samples from initial likert scale
pred_basic <- as.data.frame(predict(fa_basic,dat_onlyItems,applygetRes))
#pred_basic <- as.data.frame(predict(fa_basic,applygetRes))
#https://stackoverflow.com/questions/4145400/how-to-create-factors-from-factanal
#pred_basic <- data.frame(as.matrix(dat_onlyItems) %*% loadings(fa_basic,cutoff=0))
names(pred_basic) <- paste("Factor",1:fact,sep = "")
factors <- names(pred_basic)
all_complete_basic <- data.frame(pred_basic,all[match(all$Resp.ID,rownames(pred_basic)),])
#match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
#all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))

# Plot loadings by context
all_complete_melt <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
ggplot(all_complete_melt) + geom_boxplot(aes(x=Context,y=value,color=Context)) + facet_wrap(~variable) + coord_flip() + guides(color=F)

# error bar 
sum_stat <- all_complete_melt %>% group_by(Context,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Context[!is.na(value)])) %>%
  mutate(stdMean = stdFac/sqrt(nObs),
         CIspread=1.96*stdMean,
         LowerBoundCI = meanFac - 1.96*stdMean,
         UpperBoundCI = meanFac + 1.96*stdMean)
sum_stat$Context <- factor(sum_stat$Context,levels=c("English in Italy", "English in Germany", "Italian in Australia", "German in Australia"))
ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) + 
geom_errorbar(aes(ymin=LowerBoundCI, ymax=UpperBoundCI),width=0.2) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle("Mean +- 95% CI") 

kable(sum_stat)
Context variable meanFac stdFac nObs stdMean CIspread LowerBoundCI UpperBoundCI
English in Germany Factor1 9.180216 1.3385318 70 0.1599852 0.3135709 8.866645 9.493787
English in Germany Factor2 8.322364 0.8798173 70 0.1051583 0.2061102 8.116254 8.528474
English in Germany Factor3 1.179170 0.6342022 70 0.0758017 0.1485712 1.030599 1.327741
English in Germany Factor4 7.508108 0.9099322 70 0.1087577 0.2131651 7.294943 7.721273
English in Germany Factor5 7.780045 0.8026101 70 0.0959303 0.1880233 7.592022 7.968069
English in Germany Factor6 3.694089 0.8195410 70 0.0979539 0.1919896 3.502099 3.886079
English in Italy Factor1 10.123788 0.8109915 91 0.0850150 0.1666294 9.957159 10.290418
English in Italy Factor2 8.211752 0.8294970 91 0.0869549 0.1704316 8.041320 8.382183
English in Italy Factor3 1.515703 0.8570981 91 0.0898483 0.1761027 1.339600 1.691805
English in Italy Factor4 7.762009 0.7910125 91 0.0829206 0.1625245 7.599485 7.924534
English in Italy Factor5 8.061022 0.8048481 91 0.0843710 0.1653672 7.895655 8.226390
English in Italy Factor6 4.233386 0.6562653 91 0.0687953 0.1348388 4.098547 4.368225
German in Australia Factor1 10.151486 0.8241152 88 0.0878510 0.1721879 9.979299 10.323674
German in Australia Factor2 7.813928 1.0284663 88 0.1096349 0.2148844 7.599044 8.028813
German in Australia Factor3 1.516524 1.0230719 88 0.1090598 0.2137573 1.302767 1.730281
German in Australia Factor4 7.170152 1.0085632 88 0.1075132 0.2107259 6.959427 7.380878
German in Australia Factor5 7.452820 0.9443138 88 0.1006642 0.1973018 7.255519 7.650122
German in Australia Factor6 3.873541 0.8184000 88 0.0872417 0.1709938 3.702548 4.044535
Italian in Australia Factor1 10.160109 0.7966791 74 0.0926121 0.1815197 9.978589 10.341629
Italian in Australia Factor2 8.050933 0.8039070 74 0.0934523 0.1831665 7.867767 8.234099
Italian in Australia Factor3 1.801576 1.0142178 74 0.1179004 0.2310848 1.570491 2.032661
Italian in Australia Factor4 6.738630 0.9220862 74 0.1071903 0.2100931 6.528537 6.948723
Italian in Australia Factor5 7.572769 0.9760002 74 0.1134577 0.2223771 7.350392 7.795146
Italian in Australia Factor6 3.867960 0.8624561 74 0.1002585 0.1965066 3.671454 4.064467

1.2 Linear models testing the effect of context

pred_basic <- data.frame(pred_basic)
fact_data <- data.frame(pred_basic,all[match(all$Resp.ID,rownames(pred_basic)),c("Context","Resp.ID")])
sum(fact_data$Resp.ID != rownames(pred_basic))
[1] 0
summary(lm(Factor1 ~ Context,data=fact_data))

Call:
lm(formula = Factor1 ~ Context, data = fact_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.3077 -0.5891  0.3183  0.6631  1.7297 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                   9.1802     0.1136  80.802  < 2e-16
ContextEnglish in Italy       0.9436     0.1511   6.244 1.36e-09
ContextGerman in Australia    0.9713     0.1522   6.380 6.22e-10
ContextItalian in Australia   0.9799     0.1585   6.183 1.93e-09
                               
(Intercept)                 ***
ContextEnglish in Italy     ***
ContextGerman in Australia  ***
ContextItalian in Australia ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9506 on 319 degrees of freedom
Multiple R-squared:  0.1503,    Adjusted R-squared:  0.1423 
F-statistic: 18.81 on 3 and 319 DF,  p-value: 2.927e-11
summary(lm(Factor2 ~ Context,data=fact_data))

Call:
lm(formula = Factor2 ~ Context, data = fact_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4043 -0.6275  0.1344  0.6506  1.6875 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                   8.3224     0.1068  77.950  < 2e-16
ContextEnglish in Italy      -0.1106     0.1420  -0.779 0.436619
ContextGerman in Australia   -0.5084     0.1431  -3.554 0.000437
ContextItalian in Australia  -0.2714     0.1489  -1.822 0.069319
                               
(Intercept)                 ***
ContextEnglish in Italy        
ContextGerman in Australia  ***
ContextItalian in Australia .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8933 on 319 degrees of freedom
Multiple R-squared:  0.04484,   Adjusted R-squared:  0.03585 
F-statistic: 4.991 on 3 and 319 DF,  p-value: 0.002135
summary(lm(Factor3 ~ Context,data=fact_data))

Call:
lm(formula = Factor3 ~ Context, data = fact_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9422 -0.6078 -0.1135  0.4796  3.2376 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                   1.1792     0.1079  10.927  < 2e-16
ContextEnglish in Italy       0.3365     0.1435   2.345   0.0197
ContextGerman in Australia    0.3374     0.1446   2.333   0.0203
ContextItalian in Australia   0.6224     0.1505   4.135 4.55e-05
                               
(Intercept)                 ***
ContextEnglish in Italy     *  
ContextGerman in Australia  *  
ContextItalian in Australia ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9028 on 319 degrees of freedom
Multiple R-squared:  0.05095,   Adjusted R-squared:  0.04202 
F-statistic: 5.708 on 3 and 319 DF,  p-value: 0.0008119
summary(lm(Factor4 ~ Context,data=fact_data))

Call:
lm(formula = Factor4 ~ Context, data = fact_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1050 -0.6237  0.0094  0.7025  2.3751 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                   7.5081     0.1087  69.050  < 2e-16
ContextEnglish in Italy       0.2539     0.1446   1.756   0.0801
ContextGerman in Australia   -0.3380     0.1457  -2.320   0.0210
ContextItalian in Australia  -0.7695     0.1517  -5.073 6.66e-07
                               
(Intercept)                 ***
ContextEnglish in Italy     .  
ContextGerman in Australia  *  
ContextItalian in Australia ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9097 on 319 degrees of freedom
Multiple R-squared:  0.1517,    Adjusted R-squared:  0.1437 
F-statistic: 19.02 on 3 and 319 DF,  p-value: 2.264e-11
summary(lm(Factor5 ~ Context,data=fact_data))

Call:
lm(formula = Factor5 ~ Context, data = fact_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8463 -0.6276  0.1281  0.7365  1.5365 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                   7.7800     0.1058  73.548   <2e-16
ContextEnglish in Italy       0.2810     0.1407   1.997   0.0467
ContextGerman in Australia   -0.3272     0.1417  -2.309   0.0216
ContextItalian in Australia  -0.2073     0.1476  -1.405   0.1611
                               
(Intercept)                 ***
ContextEnglish in Italy     *  
ContextGerman in Australia  *  
ContextItalian in Australia    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.885 on 319 degrees of freedom
Multiple R-squared:  0.0697,    Adjusted R-squared:  0.06095 
F-statistic: 7.966 on 3 and 319 DF,  p-value: 3.884e-05
summary(lm(Factor6 ~ Context,data=fact_data))

Call:
lm(formula = Factor6 ~ Context, data = fact_data)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.58075 -0.47141  0.05374  0.53462  1.76449 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                  3.69409    0.09409  39.262  < 2e-16
ContextEnglish in Italy      0.53930    0.12515   4.309 2.18e-05
ContextGerman in Australia   0.17945    0.12607   1.423    0.156
ContextItalian in Australia  0.17387    0.13125   1.325    0.186
                               
(Intercept)                 ***
ContextEnglish in Italy     ***
ContextGerman in Australia     
ContextItalian in Australia    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7872 on 319 degrees of freedom
Multiple R-squared:  0.06095,   Adjusted R-squared:  0.05212 
F-statistic: 6.901 on 3 and 319 DF,  p-value: 0.0001625
summary(aov(Factor1 ~ Context,data=fact_data))
             Df Sum Sq Mean Sq F value   Pr(>F)    
Context       3   51.0  16.999   18.81 2.93e-11 ***
Residuals   319  288.2   0.904                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(aov(Factor2 ~ Context,data=fact_data))
             Df Sum Sq Mean Sq F value  Pr(>F)   
Context       3  11.95   3.983   4.991 0.00214 **
Residuals   319 254.54   0.798                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(aov(Factor3 ~ Context,data=fact_data))
             Df Sum Sq Mean Sq F value   Pr(>F)    
Context       3  13.96   4.653   5.708 0.000812 ***
Residuals   319 260.02   0.815                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(aov(Factor4 ~ Context,data=fact_data))
             Df Sum Sq Mean Sq F value   Pr(>F)    
Context       3  47.22  15.740   19.02 2.26e-11 ***
Residuals   319 264.01   0.828                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(aov(Factor5 ~ Context,data=fact_data))
             Df Sum Sq Mean Sq F value   Pr(>F)    
Context       3  18.72   6.240   7.966 3.88e-05 ***
Residuals   319 249.87   0.783                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(aov(Factor6 ~ Context,data=fact_data))
             Df Sum Sq Mean Sq F value   Pr(>F)    
Context       3  12.83   4.277   6.901 0.000163 ***
Residuals   319 197.68   0.620                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

1.3 All pairwise comparisons

Independent t-test are performed between every pair of contexts within every factor. The Bonferroni correction is used to adjust the p-values for multiple testing.

pairwise.t.test.with.t.and.df <- function (x, g, p.adjust.method = p.adjust.methods, pool.sd = !paired, 
                                           paired = FALSE, alternative = c("two.sided", "less", "greater"), 
                                           ...) 
{
    if (paired & pool.sd) 
        stop("pooling of SD is incompatible with paired tests")
    DNAME <- paste(deparse(substitute(x)), "and", deparse(substitute(g)))
    g <- factor(g)
    p.adjust.method <- match.arg(p.adjust.method)
    alternative <- match.arg(alternative)
    if (pool.sd) {
        METHOD <- "t tests with pooled SD"
        xbar <- tapply(x, g, mean, na.rm = TRUE)
        s <- tapply(x, g, sd, na.rm = TRUE)
        n <- tapply(!is.na(x), g, sum)
        degf <- n - 1
        total.degf <- sum(degf)
        pooled.sd <- sqrt(sum(s^2 * degf)/total.degf)
        compare.levels <- function(i, j) {
            dif <- xbar[i] - xbar[j]
            se.dif <- pooled.sd * sqrt(1/n[i] + 1/n[j])
            t.val <- dif/se.dif
            if (alternative == "two.sided") 
                2 * pt(-abs(t.val), total.degf)
            else pt(t.val, total.degf, lower.tail = (alternative == 
                                                         "less"))
        }
        compare.levels.t <- function(i, j) {
            dif <- xbar[i] - xbar[j]
            se.dif <- pooled.sd * sqrt(1/n[i] + 1/n[j])
            t.val = dif/se.dif 
            t.val
        }       
    }
    else {
        METHOD <- if (paired) 
            "paired t tests"
        else "t tests with non-pooled SD"
        compare.levels <- function(i, j) {
            xi <- x[as.integer(g) == i]
            xj <- x[as.integer(g) == j]
            t.test(xi, xj, paired = paired, alternative = alternative, 
                   ...)$p.value
        }
        compare.levels.t <- function(i, j) {
            xi <- x[as.integer(g) == i]
            xj <- x[as.integer(g) == j]
            t.test(xi, xj, paired = paired, alternative = alternative, 
                   ...)$statistic
        }
        compare.levels.df <- function(i, j) {
            xi <- x[as.integer(g) == i]
            xj <- x[as.integer(g) == j]
            t.test(xi, xj, paired = paired, alternative = alternative, 
                   ...)$parameter
        }
    }
    PVAL <- pairwise.table(compare.levels, levels(g), p.adjust.method)
    TVAL <- pairwise.table.t(compare.levels.t, levels(g), p.adjust.method)
    if (pool.sd) 
        DF <- total.degf
    else
        DF <- pairwise.table.t(compare.levels.df, levels(g), p.adjust.method)           
    ans <- list(method = METHOD, data.name = DNAME, p.value = PVAL, 
                p.adjust.method = p.adjust.method, t.value = TVAL, dfs = DF)
    class(ans) <- "pairwise.htest"
    ans
}
pairwise.table.t <- function (compare.levels.t, level.names, p.adjust.method) 
{
    ix <- setNames(seq_along(level.names), level.names)
    pp <- outer(ix[-1L], ix[-length(ix)], function(ivec, jvec) sapply(seq_along(ivec), 
        function(k) {
            i <- ivec[k]
            j <- jvec[k]
            if (i > j)
                compare.levels.t(i, j)               
            else NA
        }))
    pp[lower.tri(pp, TRUE)] <- pp[lower.tri(pp, TRUE)]
    pp
}

https://stackoverflow.com/questions/27544438/how-to-get-df-and-t-values-from-pairwise-t-test

#f1 <- pairwise.t.test(x = fact_data$Factor1, g = fact_data$Context,p.adjust.method = "none",pool.sd = TRUE)
#kable(f1$p.value,digits = 20)
pair.t.test <- function(x, context,fname = "F1"){
  a <- x[context %in% "English in Germany"]
  b <- x[context %in% "English in Italy"]
  c <- x[context %in% "German in Australia"]
  d <- x[context %in% "Italian in Australia"]
  
  ab <- t.test(a,b,var.equal = TRUE)
  ac <- t.test(a,c,var.equal = TRUE)
  ad <- t.test(a,d,var.equal = TRUE)
  bc <- t.test(b,c,var.equal = TRUE)
  bd <- t.test(b,d,var.equal = TRUE)
  cd <- t.test(c,d,var.equal = TRUE)
  
  test_out <- data.frame(Factor = fname,
                         Context1 = c("English in Germany","English in Germany","English in Germany",
                                      "English in Italy","English in Italy","German in Australia"),
                         Context2 = c("English in Italy","German in Australia","Italian in Australia",
                                      "German in Australia","Italian in Australia","Italian in Australia"),
                         t.value = c(ab$statistic,ac$statistic,ad$statistic,bc$statistic,bd$statistic,cd$statistic),
                         p.value = c(ab$p.value,ac$p.value,ad$p.value,bc$p.value,bd$p.value,cd$p.value),
                         estimate1 = c(ab$estimate[1],ac$estimate[1],ad$estimate[1],bc$estimate[1],bd$estimate[1],cd$estimate[1]),
                         estimate2 = c(ab$estimate[2],ac$estimate[2],ad$estimate[2],bc$estimate[2],bd$estimate[2],cd$estimate[2]),
                         confint1 = c(ab$conf.int[1],ac$conf.int[1],ad$conf.int[1],bc$conf.int[1],bd$conf.int[1],cd$conf.int[1]),
                         confint2 = c(ab$conf.int[2],ac$conf.int[2],ad$conf.int[2],bc$conf.int[2],bd$conf.int[2],cd$conf.int[2]),
                         df = c(ab$parameter,ac$parameter,ad$parameter,bc$parameter,bd$parameter,cd$parameter))
  
  return(test_out)
}
f1 <- pair.t.test(x=fact_data$Factor1,fact_data$Context,fname = "F1")
f2 <- pair.t.test(x=fact_data$Factor2,fact_data$Context,fname = "F2")
f3 <- pair.t.test(x=fact_data$Factor3,fact_data$Context,fname = "F3")
f4 <- pair.t.test(x=fact_data$Factor4,fact_data$Context,fname = "F4")
f5 <- pair.t.test(x=fact_data$Factor5,fact_data$Context,fname = "F5")
f6 <- pair.t.test(x=fact_data$Factor6,fact_data$Context,fname = "F6")
tott <- rbind(f1,f2,f3,f4,f5,f6)
tott$p.adjusted <- p.adjust(tott$p.value,method = "fdr")
kable(tott)
Factor Context1 Context2 t.value p.value estimate1 estimate2 confint1 confint2 df p.adjusted
F1 English in Germany English in Italy -5.5350351 0.0000001 9.180216 10.123788 -1.2802557 -0.6068896 159 0.0000015
F1 English in Germany German in Australia -5.6037708 0.0000001 9.180216 10.151486 -1.3136366 -0.6289051 156 0.0000015
F1 English in Germany Italian in Australia -5.3719985 0.0000003 9.180216 10.160109 -1.3404789 -0.6193081 142 0.0000028
F1 English in Italy German in Australia -0.2266294 0.8209735 10.123788 10.151486 -0.2688904 0.2134940 177 0.8956074
F1 English in Italy Italian in Australia -0.2883786 0.7734234 10.123788 10.160109 -0.2850215 0.2123799 163 0.8701013
F1 German in Australia Italian in Australia -0.0673497 0.9463874 10.151486 10.160109 -0.2614639 0.2442187 160 0.9940132
F2 English in Germany English in Italy 0.8169086 0.4152030 8.322364 8.211752 -0.1568090 0.3780334 159 0.4980776
F2 English in Germany German in Australia 3.2879532 0.0012471 8.322364 7.813928 0.2029853 0.8138862 156 0.0040814
F2 English in Germany Italian in Australia 1.9342479 0.0550713 8.322364 8.050933 -0.0059728 0.5488347 142 0.0901167
F2 English in Italy German in Australia 2.8531425 0.0048450 8.211752 7.813928 0.1226576 0.6729895 177 0.0124585
F2 English in Italy Italian in Australia 1.2557581 0.2110008 8.211752 8.050933 -0.0920617 0.4136991 163 0.2700050
F2 German in Australia Italian in Australia -1.6110000 0.1091507 7.813928 8.050933 -0.5275456 0.0535360 160 0.1571770
F3 English in Germany English in Italy -2.7550121 0.0065540 1.179170 1.515703 -0.5777844 -0.0952812 159 0.0147465
F3 English in Germany German in Australia -2.4136673 0.0169514 1.179170 1.516524 -0.6134368 -0.0612716 156 0.0358971
F3 English in Germany Italian in Australia -4.3864589 0.0000223 1.179170 1.801576 -0.9029014 -0.3419114 142 0.0000892
F3 English in Italy German in Australia -0.0058302 0.9953548 1.515703 1.516524 -0.2788562 0.2772134 177 0.9953548
F3 English in Italy Italian in Australia -1.9621658 0.0514459 1.515703 1.801576 -0.5735623 0.0018152 163 0.0881930
F3 German in Australia Italian in Australia -1.7735037 0.0780476 1.516524 1.801576 -0.6024747 0.0323703 160 0.1170714
F4 English in Germany English in Italy -1.8907362 0.0604787 7.508108 7.762009 -0.5191181 0.0113150 159 0.0946623
F4 English in Germany German in Australia 2.1840519 0.0304517 7.508108 7.170152 0.0323038 0.6436071 156 0.0548130
F4 English in Germany Italian in Australia 5.0372008 0.0000014 7.508108 6.738630 0.4675022 1.0714537 142 0.0000102
F4 English in Italy German in Australia 4.3766364 0.0000206 7.762009 7.170152 0.3249844 0.8587296 177 0.0000892
F4 English in Italy Italian in Australia 7.6715730 0.0000000 7.762009 6.738630 0.7599667 1.2867922 163 0.0000000
F4 German in Australia Italian in Australia 2.8203459 0.0054046 7.170152 6.738630 0.1293558 0.7336891 160 0.0129711
F5 English in Germany English in Italy -2.1985578 0.0293541 7.780045 8.061022 -0.5333831 -0.0285713 159 0.0548130
F5 English in Germany German in Australia 2.3101435 0.0221908 7.780045 7.452820 0.0474314 0.6070185 156 0.0443816
F5 English in Germany Italian in Australia 1.3875540 0.1674465 7.780045 7.572769 -0.0880246 0.5025768 142 0.2309192
F5 English in Italy German in Australia 4.6429211 0.0000067 8.061022 7.452820 0.3496880 0.8667163 177 0.0000378
F5 English in Italy Italian in Australia 3.5221107 0.0005555 8.061022 7.572769 0.2145206 0.7619860 163 0.0019998
F5 German in Australia Italian in Australia -0.7930895 0.4289002 7.452820 7.572769 -0.4186380 0.1787402 160 0.4980776
F6 English in Germany English in Italy -4.6366600 0.0000073 3.694089 4.233386 -0.7690120 -0.3095822 159 0.0000378
F6 English in Germany German in Australia -1.3682880 0.1731894 3.694089 3.873541 -0.4385133 0.0796086 156 0.2309192
F6 English in Germany Italian in Australia -1.2386899 0.2175041 3.694089 3.867960 -0.4513498 0.1036077 142 0.2700050
F6 English in Italy German in Australia 3.2507169 0.0013781 4.233386 3.873541 0.1413889 0.5783006 177 0.0041342
F6 English in Italy Italian in Australia 3.0896095 0.0023571 4.233386 3.867960 0.1318757 0.5989764 163 0.0065273
F6 German in Australia Italian in Australia 0.0421876 0.9664018 3.873541 3.867960 -0.2556936 0.2668563 160 0.9940132
fact_data1 <- fact_data[,c("Factor1","Context","Resp.ID")] %>% spread(key = Context, value = Factor1,drop=TRUE)

2 Demographics

Variables have been recoded and we need to do the models.

> demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
> 
> # Combine with demo variables
> pred_basic <- data.frame(pred_basic)
> demo_data <- data.frame(pred_basic,all[match(all$Resp.ID,rownames(pred_basic)),c("Resp.ID",demographics_var)])
> sum(demo_data$Resp.ID != rownames(pred_basic))
> 
> # Gender
> longData <- demo_data %>% gather(key = FactorLabel,value = FactorValue,Factor1:Factor6) %>%
+   group_by(Gender,FactorLabel) %>%
+   summarise(meanDemo = mean(FactorValue),
+             sdDemo =  sd(FactorValue))
> 
> summary(lm(Factor1 ~ prof + Gender + Age + Context,data = demo_data))
> summary(lm(Factor1 ~ Context:prof,data = demo_data))
> summary(lm(Factor2 ~ prof + Context,data = demo_data))
> summary(lm(Factor3 ~ prof + Context,data = demo_data))
> 
> summary(lm(Factor1 ~ origin + Gender + Age + Context,data = demo_data))
> summary(lm(Factor1 ~ Context:prof,data = demo_data))
> summary(lm(Factor2 ~ prof + Context,data = demo_data))
> summary(lm(Factor3 ~ prof + Context,data = demo_data))
> 
> 
> table(dat_fac_demo$L1) # to be changed
> dat_fac_demo$L1_expected <- ifelse(as.character(dat_fac_demo$L1) %in% c("German","Italian","English"),"Yes","No")
> table(dat_fac_demo$L1_expected)
> 
> table(dat_fac_demo$speak.other.L2) # to be changed
> dat_fac_demo$speak.other.L2_binary <- ifelse(!is.na(dat_fac_demo$speak.other.L2) & 
+                                       !(dat_fac_demo$speak.other.L2 %in% c("Yes","No")),"Yes",as.character(all$speak.other.L2))
> table(dat_fac_demo$speak.other.L2_binary)
> 
> #table(dat_fac_demo$study.other.L2) # to be changed
> 
> table(dat_fac_demo$origins)
> 
> table(dat_fac_demo$year.studyL2) # da vedere only for Australian contexts
> 
> table(dat_fac_demo$degree) # to be tried- only interesting for Australia
> # Merge BA in Anglistik  with BA in Nordamerikastudien
> dat_fac_demo$degree1 <- dat_fac_demo$degree
> dat_fac_demo$degree1[dat_fac_demo$degree1 %in% "BA in Nordamerikastudien"] <- "BA in Anglistik"
> 
> table(dat_fac_demo$prof)
> table(dat_fac_demo$L2.VCE)
> 
> #table(dat_fac_demo$other5.other.ways) # to be changed
demo_melt <- melt(all_complete_basic,id.vars = c("Age","Gender","origins","study.year","prof","L2.VCE","Context"),measure.vars = factors)
# age
ageStat <- demo_melt %>% group_by(Context,Age,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Age[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
ageStat$Demo <- "Age"
colnames(ageStat)[2] <- "levels"
ageStat <- data.frame(ageStat)
# Gender
GenderStat <- demo_melt %>% group_by(Context,Gender,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Gender[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
GenderStat$Demo <- "Gender"
colnames(GenderStat)[2] <- "levels"
GenderStat <- data.frame(GenderStat)
# origins
originsStat <- demo_melt %>% group_by(Context,origins,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(origins[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
originsStat$Demo <- "origins"
colnames(originsStat)[2] <- "levels"
originsStat <- data.frame(originsStat)
# study.year
study.yearStat <- demo_melt %>% group_by(Context,study.year,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(study.year[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
study.yearStat$Demo <- "Study Year"
colnames(study.yearStat)[2] <- "levels"
study.yearStat <- data.frame(study.yearStat)
# prof
profStat <- demo_melt %>% group_by(Context,prof,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(prof[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
profStat$Demo <- "Proficiency"
colnames(profStat)[2] <- "levels"
profStat$levels <- as.character(profStat$levels)
profStat <- data.frame(profStat)
# L2.VCE
L2.VCEStat <- demo_melt %>% group_by(Context,L2.VCE,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(L2.VCE[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
Factor `L2.VCE` contains implicit NA, consider using `forcats::fct_explicit_na`Factor `L2.VCE` contains implicit NA, consider using `forcats::fct_explicit_na`
L2.VCEStat$Demo <- "L2.VCE"
colnames(L2.VCEStat)[2] <- "levels"
L2.VCEStat$levels <- as.character(L2.VCEStat$levels)
L2.VCEStat <- data.frame(L2.VCEStat)
##################
# Combine stats
##################
combine_stat <- rbind(data.frame(L2.VCEStat),data.frame(profStat),study.yearStat,originsStat,ageStat,GenderStat)

2.0.1 Tables

  • Age
kable(ageStat)
Context levels variable meanFac stdFac nObs seMean CI95 Demo
English in Germany 18-25 Factor1 9.180216 1.3385318 70 0.1599852 0.3135709 Age
English in Germany 18-25 Factor2 8.322364 0.8798173 70 0.1051583 0.2061102 Age
English in Germany 18-25 Factor3 1.179170 0.6342022 70 0.0758017 0.1485712 Age
English in Germany 18-25 Factor4 7.508108 0.9099322 70 0.1087577 0.2131651 Age
English in Germany 18-25 Factor5 7.780045 0.8026101 70 0.0959303 0.1880233 Age
English in Germany 18-25 Factor6 3.694089 0.8195410 70 0.0979539 0.1919896 Age
English in Italy 18-25 Factor1 10.141992 0.8087193 88 0.0862098 0.1689712 Age
English in Italy 18-25 Factor2 8.250595 0.8114771 88 0.0865037 0.1695473 Age
English in Italy 18-25 Factor3 1.486580 0.8511478 88 0.0907327 0.1778360 Age
English in Italy 18-25 Factor4 7.766802 0.7884956 88 0.0840539 0.1647457 Age
English in Italy 18-25 Factor5 8.068912 0.8094252 88 0.0862850 0.1691186 Age
English in Italy 18-25 Factor6 4.246956 0.6412113 88 0.0683533 0.1339726 Age
English in Italy 26-30 Factor1 9.589809 0.8392664 3 0.4845507 0.9497193 Age
English in Italy 26-30 Factor2 7.072339 0.5519274 3 0.3186555 0.6245647 Age
English in Italy 26-30 Factor3 2.369975 0.6419463 3 0.3706279 0.7264306 Age
English in Italy 26-30 Factor4 7.621437 1.0396314 3 0.6002315 1.1764537 Age
English in Italy 26-30 Factor5 7.829605 0.7531231 3 0.4348158 0.8522390 Age
English in Italy 26-30 Factor6 3.835341 1.1179977 3 0.6454763 1.2651335 Age
German in Australia 18-25 Factor1 10.146344 0.8317562 86 0.0896906 0.1757935 Age
German in Australia 18-25 Factor2 7.797093 1.0343513 86 0.1115370 0.2186125 Age
German in Australia 18-25 Factor3 1.475515 0.9722664 86 0.1048422 0.2054907 Age
German in Australia 18-25 Factor4 7.172292 1.0040893 86 0.1082737 0.2122165 Age
German in Australia 18-25 Factor5 7.457453 0.9514763 86 0.1026003 0.2010967 Age
German in Australia 18-25 Factor6 3.872224 0.8274412 86 0.0892253 0.1748816 Age
German in Australia 26-30 Factor1 10.674935 NA 1 NA NA Age
German in Australia 26-30 Factor2 8.463005 NA 1 NA NA Age
German in Australia 26-30 Factor3 1.805787 NA 1 NA NA Age
German in Australia 26-30 Factor4 5.898631 NA 1 NA NA Age
German in Australia 26-30 Factor5 7.777126 NA 1 NA NA Age
German in Australia 26-30 Factor6 4.114784 NA 1 NA NA Age
German in Australia 31-35 Factor1 10.070293 NA 1 NA NA Age
German in Australia 31-35 Factor2 8.612649 NA 1 NA NA Age
German in Australia 31-35 Factor3 4.754093 NA 1 NA NA Age
German in Australia 31-35 Factor4 8.257635 NA 1 NA NA Age
German in Australia 31-35 Factor5 6.730146 NA 1 NA NA Age
German in Australia 31-35 Factor6 3.745585 NA 1 NA NA Age
Italian in Australia 18-25 Factor1 10.160109 0.7966791 74 0.0926121 0.1815197 Age
Italian in Australia 18-25 Factor2 8.050933 0.8039070 74 0.0934523 0.1831665 Age
Italian in Australia 18-25 Factor3 1.801576 1.0142178 74 0.1179004 0.2310848 Age
Italian in Australia 18-25 Factor4 6.738630 0.9220862 74 0.1071903 0.2100931 Age
Italian in Australia 18-25 Factor5 7.572769 0.9760002 74 0.1134577 0.2223771 Age
Italian in Australia 18-25 Factor6 3.867960 0.8624561 74 0.1002585 0.1965066 Age
  • Gender
kable(GenderStat)
Context levels variable meanFac stdFac nObs seMean CI95 Demo
English in Germany Female Factor1 9.375934 0.9765822 52 0.1354276 0.2654381 Gender
English in Germany Female Factor2 8.447988 0.8599845 52 0.1192584 0.2337465 Gender
English in Germany Female Factor3 1.206849 0.6015639 52 0.0834219 0.1635069 Gender
English in Germany Female Factor4 7.505623 0.9642943 52 0.1337236 0.2620982 Gender
English in Germany Female Factor5 7.799138 0.8308182 52 0.1152137 0.2258189 Gender
English in Germany Female Factor6 3.692953 0.8296887 52 0.1150571 0.2255120 Gender
English in Germany Male Factor1 8.497724 1.9850155 17 0.4814370 0.9436164 Gender
English in Germany Male Factor2 7.971708 0.8823214 17 0.2139944 0.4194290 Gender
English in Germany Male Factor3 1.088385 0.7544056 17 0.1829702 0.3586217 Gender
English in Germany Male Factor4 7.452684 0.7291647 17 0.1768484 0.3466229 Gender
English in Germany Male Factor5 7.743595 0.7526882 17 0.1825537 0.3578053 Gender
English in Germany Male Factor6 3.725440 0.8289583 17 0.2010519 0.3940618 Gender
English in Germany Other Factor1 10.605248 NA 1 NA NA Gender
English in Germany Other Factor2 7.751078 NA 1 NA NA Gender
English in Germany Other Factor3 1.283201 NA 1 NA NA Gender
English in Germany Other Factor4 8.579509 NA 1 NA NA Gender
English in Germany Other Factor5 7.406884 NA 1 NA NA Gender
English in Germany Other Factor6 3.220171 NA 1 NA NA Gender
English in Italy Female Factor1 10.186269 0.8044744 76 0.0922795 0.1808679 Gender
English in Italy Female Factor2 8.334574 0.7981082 76 0.0915493 0.1794366 Gender
English in Italy Female Factor3 1.468399 0.8543417 76 0.0979997 0.1920794 Gender
English in Italy Female Factor4 7.791795 0.7532383 76 0.0864024 0.1693486 Gender
English in Italy Female Factor5 8.173769 0.7650786 76 0.0877605 0.1720106 Gender
English in Italy Female Factor6 4.285421 0.6212523 76 0.0712625 0.1396745 Gender
English in Italy Male Factor1 9.751409 0.7942668 14 0.2122767 0.4160624 Gender
English in Italy Male Factor2 7.542614 0.7198600 14 0.1923907 0.3770857 Gender
English in Italy Male Factor3 1.796286 0.8766142 14 0.2342850 0.4591986 Gender
English in Italy Male Factor4 7.572245 1.0013242 14 0.2676151 0.5245257 Gender
English in Italy Male Factor5 7.413260 0.7498194 14 0.2003977 0.3927794 Gender
English in Italy Male Factor6 3.963581 0.8113917 14 0.2168535 0.4250329 Gender
English in Italy Other Factor1 10.588553 NA 1 NA NA Gender
English in Italy Other Factor2 8.245202 NA 1 NA NA Gender
English in Italy Other Factor3 1.182602 NA 1 NA NA Gender
English in Italy Other Factor4 8.155013 NA 1 NA NA Gender
English in Italy Other Factor5 8.560954 NA 1 NA NA Gender
English in Italy Other Factor6 4.056026 NA 1 NA NA Gender
German in Australia Female Factor1 10.168562 0.8395033 62 0.1066170 0.2089694 Gender
German in Australia Female Factor2 7.866677 1.0376375 62 0.1317801 0.2582890 Gender
German in Australia Female Factor3 1.608489 1.0137556 62 0.1287471 0.2523443 Gender
German in Australia Female Factor4 7.165064 0.9795896 62 0.1244080 0.2438397 Gender
German in Australia Female Factor5 7.476764 0.8955129 62 0.1137303 0.2229113 Gender
German in Australia Female Factor6 3.894508 0.8770328 62 0.1113833 0.2183112 Gender
German in Australia Male Factor1 10.084504 0.8058951 25 0.1611790 0.3159109 Gender
German in Australia Male Factor2 7.692401 1.0358125 25 0.2071625 0.4060385 Gender
German in Australia Male Factor3 1.286442 1.0512047 25 0.2102409 0.4120723 Gender
German in Australia Male Factor4 7.206900 1.1098389 25 0.2219678 0.4350569 Gender
German in Australia Male Factor5 7.338295 1.0487657 25 0.2097531 0.4111161 Gender
German in Australia Male Factor6 3.809513 0.6809965 25 0.1361993 0.2669506 Gender
German in Australia Other Factor1 10.767338 NA 1 NA NA Gender
German in Australia Other Factor2 7.581668 NA 1 NA NA Gender
German in Australia Other Factor3 1.566750 NA 1 NA NA Gender
German in Australia Other Factor4 6.566955 NA 1 NA NA Gender
German in Australia Other Factor5 8.831433 NA 1 NA NA Gender
German in Australia Other Factor6 4.174308 NA 1 NA NA Gender
Italian in Australia Female Factor1 10.297225 0.7153860 58 0.0939348 0.1841122 Gender
Italian in Australia Female Factor2 8.100776 0.8162308 58 0.1071764 0.2100657 Gender
Italian in Australia Female Factor3 1.744322 1.0319832 58 0.1355060 0.2655918 Gender
Italian in Australia Female Factor4 6.760356 0.9430673 58 0.1238308 0.2427084 Gender
Italian in Australia Female Factor5 7.589786 0.9483878 58 0.1245294 0.2440777 Gender
Italian in Australia Female Factor6 3.912879 0.9089066 58 0.1193453 0.2339168 Gender
Italian in Australia Male Factor1 9.593882 0.8851820 15 0.2285530 0.4479639 Gender
Italian in Australia Male Factor2 7.839940 0.7706787 15 0.1989884 0.3900172 Gender
Italian in Australia Male Factor3 2.051057 0.9667621 15 0.2496169 0.4892491 Gender
Italian in Australia Male Factor4 6.598503 0.8595188 15 0.2219268 0.4349765 Gender
Italian in Australia Male Factor5 7.432109 1.0920342 15 0.2819620 0.5526455 Gender
Italian in Australia Male Factor6 3.694445 0.6888211 15 0.1778528 0.3485916 Gender
Italian in Australia Other Factor1 10.700782 NA 1 NA NA Gender
Italian in Australia Other Factor2 8.324951 NA 1 NA NA Gender
Italian in Australia Other Factor3 1.380134 NA 1 NA NA Gender
Italian in Australia Other Factor4 7.580407 NA 1 NA NA Gender
Italian in Australia Other Factor5 8.695675 NA 1 NA NA Gender
Italian in Australia Other Factor6 3.865389 NA 1 NA NA Gender
  • origins
kable(originsStat)
Context levels variable meanFac stdFac nObs seMean CI95 Demo
English in Germany No Factor1 9.142259 1.3714988 65 0.1701135 0.3334224 origins
English in Germany No Factor2 8.281116 0.8704514 65 0.1079662 0.2116138 origins
English in Germany No Factor3 1.181588 0.6255740 65 0.0775929 0.1520821 origins
English in Germany No Factor4 7.494999 0.9291426 65 0.1152460 0.2258821 origins
English in Germany No Factor5 7.771619 0.8256632 65 0.1024109 0.2007254 origins
English in Germany No Factor6 3.709887 0.8005854 65 0.0993004 0.1946288 origins
English in Germany Yes Factor1 9.673656 0.6945173 5 0.3105976 0.6087713 origins
English in Germany Yes Factor2 8.858589 0.9180493 5 0.4105641 0.8047057 origins
English in Germany Yes Factor3 1.147741 0.8217959 5 0.3675183 0.7203359 origins
English in Germany Yes Factor4 7.678528 0.6562043 5 0.2934635 0.5751884 origins
English in Germany Yes Factor5 7.889584 0.4341531 5 0.1941592 0.3805519 origins
English in Germany Yes Factor6 3.488721 1.1287833 5 0.5048072 0.9894221 origins
English in Italy No Factor1 10.114543 0.8106982 90 0.0854551 0.1674920 origins
English in Italy No Factor2 8.206219 0.8324542 90 0.0877484 0.1719868 origins
English in Italy No Factor3 1.496846 0.8427046 90 0.0888289 0.1741046 origins
English in Italy No Factor4 7.758974 0.7949109 90 0.0837910 0.1642303 origins
English in Italy No Factor5 8.052053 0.8047709 90 0.0848303 0.1662674 origins
English in Italy No Factor6 4.240191 0.6567059 90 0.0692229 0.1356768 origins
English in Italy Yes Factor1 10.955859 NA 1 NA NA origins
English in Italy Yes Factor2 8.709651 NA 1 NA NA origins
English in Italy Yes Factor3 3.212791 NA 1 NA NA origins
English in Italy Yes Factor4 8.035203 NA 1 NA NA origins
English in Italy Yes Factor5 8.868245 NA 1 NA NA origins
English in Italy Yes Factor6 3.620988 NA 1 NA NA origins
German in Australia No Factor1 10.231949 0.7717087 63 0.0972262 0.1905633 origins
German in Australia No Factor2 7.618197 1.0552275 63 0.1329462 0.2605745 origins
German in Australia No Factor3 1.370109 0.9086951 63 0.1144848 0.2243903 origins
German in Australia No Factor4 7.053960 1.0218361 63 0.1287392 0.2523289 origins
German in Australia No Factor5 7.500587 0.8429098 63 0.1061966 0.2081454 origins
German in Australia No Factor6 3.738314 0.7921048 63 0.0997958 0.1955998 origins
German in Australia Yes Factor1 9.948720 0.9293495 25 0.1858699 0.3643050 origins
German in Australia Yes Factor2 8.307170 0.7770288 25 0.1554058 0.3045953 origins
German in Australia Yes Factor3 1.885491 1.2095416 25 0.2419083 0.4741403 origins
German in Australia Yes Factor4 7.462958 0.9301706 25 0.1860341 0.3646269 origins
German in Australia Yes Factor5 7.332448 1.1730232 25 0.2346046 0.4598251 origins
German in Australia Yes Factor6 4.214316 0.7988231 25 0.1597646 0.3131387 origins
Italian in Australia No Factor1 10.084062 0.8466602 36 0.1411100 0.2765757 origins
Italian in Australia No Factor2 7.914387 0.8738110 36 0.1456352 0.2854449 origins
Italian in Australia No Factor3 1.613830 1.0026692 36 0.1671115 0.3275386 origins
Italian in Australia No Factor4 6.764422 0.8983703 36 0.1497284 0.2934676 origins
Italian in Australia No Factor5 7.650693 0.9790895 36 0.1631816 0.3198359 origins
Italian in Australia No Factor6 4.048849 0.8050751 36 0.1341792 0.2629912 origins
Italian in Australia Yes Factor1 10.232154 0.7504652 38 0.1217415 0.2386134 origins
Italian in Australia Yes Factor2 8.180293 0.7193513 38 0.1166942 0.2287206 origins
Italian in Australia Yes Factor3 1.979441 1.0058256 38 0.1631665 0.3198063 origins
Italian in Australia Yes Factor4 6.714195 0.9554068 38 0.1549875 0.3037754 origins
Italian in Australia Yes Factor5 7.498947 0.9803588 38 0.1590352 0.3117090 origins
Italian in Australia Yes Factor6 3.696591 0.8901960 38 0.1444089 0.2830414 origins
  • study.year
kable(study.yearStat)
Context levels variable meanFac stdFac nObs seMean CI95 Demo
English in Germany 1st semester Factor1 9.180216 1.3385318 70 0.1599852 0.3135709 Study Year
English in Germany 1st semester Factor2 8.322364 0.8798173 70 0.1051583 0.2061102 Study Year
English in Germany 1st semester Factor3 1.179170 0.6342022 70 0.0758017 0.1485712 Study Year
English in Germany 1st semester Factor4 7.508108 0.9099322 70 0.1087577 0.2131651 Study Year
English in Germany 1st semester Factor5 7.780045 0.8026101 70 0.0959303 0.1880233 Study Year
English in Germany 1st semester Factor6 3.694089 0.8195410 70 0.0979539 0.1919896 Study Year
English in Italy 1st year Factor1 10.123788 0.8109915 91 0.0850150 0.1666294 Study Year
English in Italy 1st year Factor2 8.211752 0.8294970 91 0.0869549 0.1704316 Study Year
English in Italy 1st year Factor3 1.515703 0.8570981 91 0.0898483 0.1761027 Study Year
English in Italy 1st year Factor4 7.762009 0.7910125 91 0.0829206 0.1625245 Study Year
English in Italy 1st year Factor5 8.061022 0.8048481 91 0.0843710 0.1653672 Study Year
English in Italy 1st year Factor6 4.233386 0.6562653 91 0.0687953 0.1348388 Study Year
German in Australia 1st year Factor1 10.151486 0.8241152 88 0.0878510 0.1721879 Study Year
German in Australia 1st year Factor2 7.813928 1.0284663 88 0.1096349 0.2148844 Study Year
German in Australia 1st year Factor3 1.516524 1.0230719 88 0.1090598 0.2137573 Study Year
German in Australia 1st year Factor4 7.170152 1.0085632 88 0.1075132 0.2107259 Study Year
German in Australia 1st year Factor5 7.452820 0.9443138 88 0.1006642 0.1973018 Study Year
German in Australia 1st year Factor6 3.873541 0.8184000 88 0.0872417 0.1709938 Study Year
Italian in Australia 1st year Factor1 10.160109 0.7966791 74 0.0926121 0.1815197 Study Year
Italian in Australia 1st year Factor2 8.050933 0.8039070 74 0.0934523 0.1831665 Study Year
Italian in Australia 1st year Factor3 1.801576 1.0142178 74 0.1179004 0.2310848 Study Year
Italian in Australia 1st year Factor4 6.738630 0.9220862 74 0.1071903 0.2100931 Study Year
Italian in Australia 1st year Factor5 7.572769 0.9760002 74 0.1134577 0.2223771 Study Year
Italian in Australia 1st year Factor6 3.867960 0.8624561 74 0.1002585 0.1965066 Study Year
  • prof
kable(profStat)
Context levels variable meanFac stdFac nObs seMean CI95 Demo
English in Germany Advanced Factor1 8.9718180 1.5713176 38 0.2549014 0.4996067 Proficiency
English in Germany Advanced Factor2 8.5188156 0.8260854 38 0.1340088 0.2626572 Proficiency
English in Germany Advanced Factor3 1.1248261 0.6062369 38 0.0983446 0.1927554 Proficiency
English in Germany Advanced Factor4 7.6658829 0.8812894 38 0.1429640 0.2802095 Proficiency
English in Germany Advanced Factor5 7.9375503 0.7265937 38 0.1178691 0.2310234 Proficiency
English in Germany Advanced Factor6 3.6131235 0.8709985 38 0.1412946 0.2769375 Proficiency
English in Germany Intermediate Factor1 9.4658054 1.2064684 5 0.5395491 1.0575162 Proficiency
English in Germany Intermediate Factor2 8.3971644 1.1238905 5 0.5026191 0.9851335 Proficiency
English in Germany Intermediate Factor3 1.1462753 0.3615941 5 0.1617098 0.3169512 Proficiency
English in Germany Intermediate Factor4 7.5035826 0.5481148 5 0.2451244 0.4804438 Proficiency
English in Germany Intermediate Factor5 7.3447820 0.7218051 5 0.3228011 0.6326901 Proficiency
English in Germany Intermediate Factor6 4.2208877 0.3896576 5 0.1742602 0.3415500 Proficiency
English in Germany Upper-intermediate Factor1 9.4206289 0.9370504 27 0.1803354 0.3534575 Proficiency
English in Germany Upper-intermediate Factor2 8.0320244 0.8624412 27 0.1659769 0.3253147 Proficiency
English in Germany Upper-intermediate Factor3 1.2617455 0.7160135 27 0.1377969 0.2700818 Proficiency
English in Germany Upper-intermediate Factor4 7.2868921 0.9790993 27 0.1884277 0.3693184 Proficiency
English in Germany Upper-intermediate Factor5 7.6389760 0.8858635 27 0.1704845 0.3341496 Proficiency
English in Germany Upper-intermediate Factor6 3.7104854 0.7850227 27 0.1510777 0.2961123 Proficiency
English in Italy Advanced Factor1 10.0450022 0.8021570 23 0.1672613 0.3278322 Proficiency
English in Italy Advanced Factor2 8.3272071 0.7027466 23 0.1465328 0.2872043 Proficiency
English in Italy Advanced Factor3 1.2983018 0.6492239 23 0.1353725 0.2653302 Proficiency
English in Italy Advanced Factor4 7.6913472 0.9783794 23 0.2040062 0.3998522 Proficiency
English in Italy Advanced Factor5 8.3835120 0.7459519 23 0.1555417 0.3048618 Proficiency
English in Italy Advanced Factor6 4.0067942 0.8215462 23 0.1713042 0.3357563 Proficiency
English in Italy Elementary Factor1 10.7316730 0.2869470 2 0.2029022 0.3976882 Proficiency
English in Italy Elementary Factor2 6.6947733 0.1118463 2 0.0790873 0.1550110 Proficiency
English in Italy Elementary Factor3 0.5452615 0.0207356 2 0.0146623 0.0287381 Proficiency
English in Italy Elementary Factor4 6.9603545 0.2167864 2 0.1532911 0.3004506 Proficiency
English in Italy Elementary Factor5 7.0648783 0.4524283 2 0.3199151 0.6270336 Proficiency
English in Italy Elementary Factor6 3.8789953 0.4947548 2 0.3498445 0.6856951 Proficiency
English in Italy Intermediate Factor1 9.8617793 0.9736565 9 0.3245522 0.6361222 Proficiency
English in Italy Intermediate Factor2 8.0702928 0.6788276 9 0.2262759 0.4435007 Proficiency
English in Italy Intermediate Factor3 1.4754908 1.0336667 9 0.3445556 0.6753289 Proficiency
English in Italy Intermediate Factor4 7.5815590 0.7990103 9 0.2663368 0.5220201 Proficiency
English in Italy Intermediate Factor5 7.7851747 0.8353883 9 0.2784628 0.5457870 Proficiency
English in Italy Intermediate Factor6 4.3672887 0.3214393 9 0.1071464 0.2100070 Proficiency
English in Italy Upper-intermediate Factor1 10.1756196 0.7986421 57 0.1057827 0.2073342 Proficiency
English in Italy Upper-intermediate Factor2 8.2407273 0.8683437 57 0.1150149 0.2254293 Proficiency
English in Italy Upper-intermediate Factor3 1.6438258 0.8901621 57 0.1179049 0.2310935 Proficiency
English in Italy Upper-intermediate Factor4 7.8471426 0.7070239 57 0.0936476 0.1835493 Proficiency
English in Italy Upper-intermediate Factor5 8.0094025 0.7912184 57 0.1047994 0.2054069 Proficiency
English in Italy Upper-intermediate Factor6 4.3161104 0.6102990 57 0.0808361 0.1584387 Proficiency
German in Australia Advanced Factor1 10.6305008 0.1903613 4 0.0951807 0.1865541 Proficiency
German in Australia Advanced Factor2 8.5881502 0.9889888 4 0.4944944 0.9692090 Proficiency
German in Australia Advanced Factor3 2.0015290 1.6060287 4 0.8030144 1.5739081 Proficiency
German in Australia Advanced Factor4 7.7057327 1.0522989 4 0.5261494 1.0312529 Proficiency
German in Australia Advanced Factor5 7.6357323 1.1159707 4 0.5579853 1.0936513 Proficiency
German in Australia Advanced Factor6 3.7133896 1.0305066 4 0.5152533 1.0098965 Proficiency
German in Australia Elementary Factor1 10.1168077 0.9064044 32 0.1602312 0.3140531 Proficiency
German in Australia Elementary Factor2 7.6501811 1.1416160 32 0.2018111 0.3955498 Proficiency
German in Australia Elementary Factor3 1.7631737 1.1534218 32 0.2038981 0.3996402 Proficiency
German in Australia Elementary Factor4 7.2927306 1.0145114 32 0.1793420 0.3515103 Proficiency
German in Australia Elementary Factor5 7.2999255 1.0797485 32 0.1908744 0.3741138 Proficiency
German in Australia Elementary Factor6 4.0011990 0.8029138 32 0.1419364 0.2781954 Proficiency
German in Australia Intermediate Factor1 10.2298341 0.7820790 25 0.1564158 0.3065750 Proficiency
German in Australia Intermediate Factor2 7.8832030 0.8604999 25 0.1721000 0.3373159 Proficiency
German in Australia Intermediate Factor3 1.4811441 0.7509894 25 0.1501979 0.2943879 Proficiency
German in Australia Intermediate Factor4 7.2424688 0.9742859 25 0.1948572 0.3819201 Proficiency
German in Australia Intermediate Factor5 7.5145537 0.9286726 25 0.1857345 0.3640397 Proficiency
German in Australia Intermediate Factor6 3.8848371 0.7601886 25 0.1520377 0.2979939 Proficiency
German in Australia Upper-intermediate Factor1 10.0490780 0.8189491 27 0.1576068 0.3089094 Proficiency
German in Australia Upper-intermediate Factor2 7.8291557 1.0290861 27 0.1980477 0.3881735 Proficiency
German in Australia Upper-intermediate Factor3 1.1851056 0.9335779 27 0.1796672 0.3521476 Proficiency
German in Australia Upper-intermediate Factor4 6.8785697 1.0082746 27 0.1940425 0.3803234 Proficiency
German in Australia Upper-intermediate Factor5 7.5497705 0.7788880 27 0.1498971 0.2937982 Proficiency
German in Australia Upper-intermediate Factor6 3.7355109 0.8790105 27 0.1691657 0.3315647 Proficiency
Italian in Australia Elementary Factor1 10.3507740 0.5949701 29 0.1104832 0.2165470 Proficiency
Italian in Australia Elementary Factor2 8.0229350 0.8425050 29 0.1564493 0.3066405 Proficiency
Italian in Australia Elementary Factor3 1.5731498 1.0289672 29 0.1910744 0.3745059 Proficiency
Italian in Australia Elementary Factor4 6.5818719 0.9056561 29 0.1681761 0.3296252 Proficiency
Italian in Australia Elementary Factor5 7.5793151 0.8975255 29 0.1666663 0.3266660 Proficiency
Italian in Australia Elementary Factor6 3.6640290 0.9666588 29 0.1795040 0.3518279 Proficiency
Italian in Australia Intermediate Factor1 10.0855631 0.8814353 29 0.1636784 0.3208097 Proficiency
Italian in Australia Intermediate Factor2 8.0082902 0.8190878 29 0.1521008 0.2981176 Proficiency
Italian in Australia Intermediate Factor3 1.7371213 1.0600615 29 0.1968485 0.3858230 Proficiency
Italian in Australia Intermediate Factor4 6.9176281 0.9645575 29 0.1791138 0.3510631 Proficiency
Italian in Australia Intermediate Factor5 7.5780978 1.0924933 29 0.2028709 0.3976270 Proficiency
Italian in Australia Intermediate Factor6 4.0383131 0.7613362 29 0.1413766 0.2770981 Proficiency
Italian in Australia Upper-intermediate Factor1 9.9496433 0.9217052 16 0.2304263 0.4516356 Proficiency
Italian in Australia Upper-intermediate Factor2 8.1789692 0.7384464 16 0.1846116 0.3618387 Proficiency
Italian in Australia Upper-intermediate Factor3 2.3324244 0.7228639 16 0.1807160 0.3542033 Proficiency
Italian in Australia Upper-intermediate Factor4 6.6983198 0.8711522 16 0.2177881 0.4268646 Proficiency
Italian in Australia Upper-intermediate Factor5 7.5512469 0.9505781 16 0.2376445 0.4657833 Proficiency
Italian in Australia Upper-intermediate Factor6 3.9288207 0.8082490 16 0.2020622 0.3960420 Proficiency
  • L2.VCE
kable(L2.VCEStat)
Context levels variable meanFac stdFac nObs seMean CI95 Demo
English in Germany NA Factor1 9.180216 1.3385318 70 0.1599852 0.3135709 L2.VCE
English in Germany NA Factor2 8.322364 0.8798173 70 0.1051583 0.2061102 L2.VCE
English in Germany NA Factor3 1.179170 0.6342022 70 0.0758017 0.1485712 L2.VCE
English in Germany NA Factor4 7.508108 0.9099322 70 0.1087577 0.2131651 L2.VCE
English in Germany NA Factor5 7.780045 0.8026101 70 0.0959303 0.1880233 L2.VCE
English in Germany NA Factor6 3.694089 0.8195410 70 0.0979539 0.1919896 L2.VCE
English in Italy NA Factor1 10.123788 0.8109915 91 0.0850150 0.1666294 L2.VCE
English in Italy NA Factor2 8.211752 0.8294970 91 0.0869549 0.1704316 L2.VCE
English in Italy NA Factor3 1.515703 0.8570981 91 0.0898483 0.1761027 L2.VCE
English in Italy NA Factor4 7.762009 0.7910125 91 0.0829206 0.1625245 L2.VCE
English in Italy NA Factor5 8.061022 0.8048481 91 0.0843710 0.1653672 L2.VCE
English in Italy NA Factor6 4.233386 0.6562653 91 0.0687953 0.1348388 L2.VCE
German in Australia No Factor1 10.049914 0.9790703 27 0.1884222 0.3693075 L2.VCE
German in Australia No Factor2 7.820436 1.2438692 27 0.2393827 0.4691902 L2.VCE
German in Australia No Factor3 1.861283 1.2187040 27 0.2345397 0.4596978 L2.VCE
German in Australia No Factor4 7.372187 1.1104227 27 0.2137009 0.4188538 L2.VCE
German in Australia No Factor5 7.258649 1.1235823 27 0.2162335 0.4238177 L2.VCE
German in Australia No Factor6 4.024900 0.9492583 27 0.1826848 0.3580623 L2.VCE
German in Australia Yes Factor1 10.213215 0.7616911 48 0.1099406 0.2154837 L2.VCE
German in Australia Yes Factor2 7.938875 0.8524114 48 0.1230350 0.2411486 L2.VCE
German in Australia Yes Factor3 1.440850 0.9493849 48 0.1370319 0.2685825 L2.VCE
German in Australia Yes Factor4 7.113726 1.0136097 48 0.1463020 0.2867518 L2.VCE
German in Australia Yes Factor5 7.468157 0.8535962 48 0.1232060 0.2414838 L2.VCE
German in Australia Yes Factor6 3.822268 0.7191611 48 0.1038020 0.2034518 L2.VCE
German in Australia NA Factor1 10.134523 0.7320653 13 0.2030384 0.3979552 L2.VCE
German in Australia NA Factor2 7.339069 1.0787274 13 0.2991852 0.5864029 L2.VCE
German in Australia NA Factor3 1.079900 0.5859190 13 0.1625047 0.3185092 L2.VCE
German in Australia NA Factor4 6.958886 0.7253853 13 0.2011857 0.3943239 L2.VCE
German in Australia NA Factor5 7.799474 0.8124103 13 0.2253221 0.4416312 L2.VCE
German in Australia NA Factor6 3.748502 0.8937892 13 0.2478925 0.4858693 L2.VCE
Italian in Australia No Factor1 10.284602 0.5720927 20 0.1279238 0.2507307 L2.VCE
Italian in Australia No Factor2 7.874895 0.8346340 20 0.1866298 0.3657945 L2.VCE
Italian in Australia No Factor3 1.697145 1.1802692 20 0.2639162 0.5172758 L2.VCE
Italian in Australia No Factor4 6.743492 0.8269375 20 0.1849089 0.3624214 L2.VCE
Italian in Australia No Factor5 7.445237 0.9635308 20 0.2154520 0.4222860 L2.VCE
Italian in Australia No Factor6 3.581091 0.9805687 20 0.2192618 0.4297532 L2.VCE
Italian in Australia Yes Factor1 10.080101 0.8941083 42 0.1379639 0.2704093 L2.VCE
Italian in Australia Yes Factor2 8.072739 0.8012190 42 0.1236308 0.2423163 L2.VCE
Italian in Australia Yes Factor3 1.878319 0.9350282 42 0.1442780 0.2827848 L2.VCE
Italian in Australia Yes Factor4 6.870583 0.9809297 42 0.1513607 0.2966670 L2.VCE
Italian in Australia Yes Factor5 7.585222 1.0525838 42 0.1624172 0.3183377 L2.VCE
Italian in Australia Yes Factor6 4.053883 0.7618064 42 0.1175493 0.2303966 L2.VCE
Italian in Australia NA Factor1 10.232648 0.7802001 12 0.2252244 0.4414398 L2.VCE
Italian in Australia NA Factor2 8.268009 0.7637552 12 0.2204771 0.4321352 L2.VCE
Italian in Australia NA Factor3 1.707029 1.0533388 12 0.3040727 0.5959825 L2.VCE
Italian in Australia NA Factor4 6.268689 0.7532963 12 0.2174579 0.4262175 L2.VCE
Italian in Australia NA Factor5 7.741738 0.7260722 12 0.2095990 0.4108140 L2.VCE
Italian in Australia NA Factor6 3.695345 0.8934747 12 0.2579239 0.5055309 L2.VCE

2.0.2 Factor means with Confidence Intervals

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor1")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor1: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor2")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor2: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor3")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor3: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor4")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor4: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor5")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor5: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor6")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor6: Mean +- 95% CI") + theme_bw()

3 Other tentatives

3.1 FA with 7 factors (as from design)

# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
usable_data <- all[,usable_items]
sum(is.na(usable_data))
[1] 0
# Cronbach's alpha using consistent items across contexts
psych::alpha(usable_data,use="pairwise.complete.obs")

Reliability analysis   
Call: psych::alpha(x = usable_data, use = "pairwise.complete.obs")

 

 lower alpha upper     95% confidence boundaries
0.81 0.84 0.86 

 Reliability if an item is dropped:

 Item statistics 

Non missing response frequency for each item
                      1    2    3    4    5 miss
converse.id1       0.00 0.03 0.10 0.41 0.47    0
dream.id1          0.00 0.00 0.07 0.36 0.56    0
usewell.id1        0.00 0.02 0.11 0.46 0.42    0
whenever.id1       0.00 0.03 0.12 0.37 0.47    0
consider.ought1    0.14 0.40 0.21 0.19 0.06    0
people.ought1      0.09 0.27 0.25 0.28 0.11    0
expect.ought1      0.39 0.44 0.09 0.07 0.01    0
fail.ought1        0.27 0.46 0.16 0.10 0.01    0
enjoy.intr1        0.00 0.01 0.06 0.40 0.54    0
life.intr1         0.02 0.24 0.25 0.36 0.12    0
exciting.intr1     0.00 0.01 0.02 0.37 0.61    0
challenge.intr1    0.00 0.03 0.12 0.48 0.36    0
job.instru1        0.00 0.04 0.32 0.41 0.23    0
knowledge.instru1  0.00 0.01 0.09 0.59 0.32    0
career.instru1     0.00 0.00 0.20 0.41 0.39    0
money.instru1      0.01 0.12 0.55 0.26 0.06    0
time.integr1       0.00 0.01 0.07 0.29 0.63    0
becomelike.integr1 0.03 0.23 0.47 0.18 0.10    0
meeting.integr1    0.00 0.00 0.03 0.37 0.59    0
affinity.integr1   0.01 0.07 0.36 0.39 0.17    0
improve.prof1      0.01 0.02 0.03 0.34 0.59    0
speaking.prof1     0.00 0.01 0.00 0.28 0.71    0
reading.prof1      0.00 0.02 0.02 0.38 0.59    0
written.prof1      0.00 0.01 0.02 0.36 0.62    0
listening.prof1    0.00 0.01 0.04 0.38 0.57    0
citizen.post1      0.01 0.07 0.23 0.46 0.23    0
interact.post1     0.00 0.00 0.06 0.43 0.50    0
overseas.post1     0.00 0.01 0.02 0.34 0.63    0
globalaccess.post1 0.00 0.01 0.06 0.49 0.43    0
fact <- 7
loading_cutoff <- 0.2
fa_basic <- fa(usable_data,fact)
fa_basic
Factor Analysis using method =  minres
Call: fa(r = usable_data, nfactors = fact)
Standardized loadings (pattern matrix) based upon correlation matrix

                       MR2  MR3  MR4  MR7  MR5  MR6  MR1
SS loadings           3.30 1.86 2.00 1.95 1.89 1.79 0.85
Proportion Var        0.11 0.06 0.07 0.07 0.07 0.06 0.03
Cumulative Var        0.11 0.18 0.25 0.31 0.38 0.44 0.47
Proportion Explained  0.24 0.14 0.15 0.14 0.14 0.13 0.06
Cumulative Proportion 0.24 0.38 0.53 0.67 0.81 0.94 1.00

 With factor correlations of 
     MR2   MR3  MR4  MR7   MR5  MR6   MR1
MR2 1.00  0.08 0.10 0.08  0.20 0.24  0.04
MR3 0.08  1.00 0.07 0.08 -0.03 0.02 -0.15
MR4 0.10  0.07 1.00 0.26  0.27 0.42  0.14
MR7 0.08  0.08 0.26 1.00  0.33 0.28  0.12
MR5 0.20 -0.03 0.27 0.33  1.00 0.36  0.20
MR6 0.24  0.02 0.42 0.28  0.36 1.00  0.14
MR1 0.04 -0.15 0.14 0.12  0.20 0.14  1.00

Mean item complexity =  1.8
Test of the hypothesis that 7 factors are sufficient.

The degrees of freedom for the null model are  406  and the objective function was  10.52 with Chi Square of  3278.37
The degrees of freedom for the model are 224  and the objective function was  1.23 

The root mean square of the residuals (RMSR) is  0.03 
The df corrected root mean square of the residuals is  0.04 

The harmonic number of observations is  323 with the empirical chi square  224.23  with prob <  0.48 
The total number of observations was  323  with Likelihood Chi Square =  376.07  with prob <  8.2e-10 

Tucker Lewis Index of factoring reliability =  0.902
RMSEA index =  0.049  and the 90 % confidence intervals are  0.038 0.054
BIC =  -918.12
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy             
                                                   MR2  MR3  MR4
Correlation of (regression) scores with factors   0.95 0.90 0.90
Multiple R square of scores with factors          0.90 0.81 0.82
Minimum correlation of possible factor scores     0.80 0.62 0.63
                                                   MR7  MR5  MR6
Correlation of (regression) scores with factors   0.88 0.89 0.86
Multiple R square of scores with factors          0.77 0.79 0.74
Minimum correlation of possible factor scores     0.53 0.58 0.49
                                                   MR1
Correlation of (regression) scores with factors   0.75
Multiple R square of scores with factors          0.57
Minimum correlation of possible factor scores     0.13
# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic
a1 <- melt(a1,id.vars=c("D"))
a1$inv <- ifelse(a1$value < 0 ,"neg","pos")
a1$value[abs(a1$value) < loading_cutoff] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")

# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced
# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,usable_data))
names(pred_basic) <- paste("Factor",1:fact,sep = "")
factors <- names(pred_basic)
match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))

# Plot loadings by context
all_complete_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
library(ggplot2)
ggplot(all_complete_basic)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)

# 7 * 12 rows removed

3.2 Basic factor analysis: 6 factors following the fa.parallel suggestion

Using very relaxed cutoff of 0.2 to get rid of not important variables in each factor.

# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
usable_data <- all[,usable_items]
# From a statisticak point of view 
fap <- fa.parallel(usable_data)
Parallel analysis suggests that the number of factors =  6  and the number of components =  4 

fact <- 6
loading_cutoff <- 0.2
fa_basic <- fa(usable_data,fact)
fa_basic
Factor Analysis using method =  minres
Call: fa(r = usable_data, nfactors = fact)
Standardized loadings (pattern matrix) based upon correlation matrix

                       MR2  MR4  MR3  MR5  MR1  MR6
SS loadings           3.35 2.47 1.88 2.11 2.15 1.03
Proportion Var        0.12 0.09 0.06 0.07 0.07 0.04
Cumulative Var        0.12 0.20 0.27 0.34 0.41 0.45
Proportion Explained  0.26 0.19 0.14 0.16 0.17 0.08
Cumulative Proportion 0.26 0.45 0.59 0.76 0.92 1.00

 With factor correlations of 
     MR2  MR4   MR3  MR5   MR1  MR6
MR2 1.00 0.13  0.05 0.11  0.22 0.16
MR4 0.13 1.00  0.00 0.33  0.37 0.24
MR3 0.05 0.00  1.00 0.03 -0.05 0.11
MR5 0.11 0.33  0.03 1.00  0.39 0.16
MR1 0.22 0.37 -0.05 0.39  1.00 0.16
MR6 0.16 0.24  0.11 0.16  0.16 1.00

Mean item complexity =  1.8
Test of the hypothesis that 6 factors are sufficient.

The degrees of freedom for the null model are  406  and the objective function was  10.52 with Chi Square of  3278.37
The degrees of freedom for the model are 247  and the objective function was  1.46 

The root mean square of the residuals (RMSR) is  0.03 
The df corrected root mean square of the residuals is  0.04 

The harmonic number of observations is  323 with the empirical chi square  293.67  with prob <  0.022 
The total number of observations was  323  with Likelihood Chi Square =  448.4  with prob <  7.6e-14 

Tucker Lewis Index of factoring reliability =  0.883
RMSEA index =  0.053  and the 90 % confidence intervals are  0.043 0.058
BIC =  -978.68
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy             
                                                   MR2  MR4  MR3
Correlation of (regression) scores with factors   0.95 0.91 0.90
Multiple R square of scores with factors          0.90 0.82 0.81
Minimum correlation of possible factor scores     0.80 0.65 0.63
                                                   MR5  MR1  MR6
Correlation of (regression) scores with factors   0.88 0.89 0.78
Multiple R square of scores with factors          0.78 0.79 0.61
Minimum correlation of possible factor scores     0.55 0.57 0.22
# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic
a1 <- melt(a1,id.vars=c("D"))
a1$inv <- ifelse(a1$value < 0 ,"neg","pos")
a1$value[abs(a1$value) < loading_cutoff] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")

# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced
# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,usable_data))
names(pred_basic) <- paste("Factor",1:fact,sep = "")
factors <- names(pred_basic)
match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))

# Plot loadings by context
all_complete_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
library(ggplot2)
ggplot(all_complete_basic)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)

# 7 * 12 rows removed
# error bar 
sum_stat <- all_complete_basic %>% group_by(Context,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Context[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle("Mean +- 95% CI")

ggplot(sum_stat,aes(x=variable,y=meanFac,colour=variable)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~Context,scales="free_y") + 
  geom_point() + ggtitle("Mean +- 95% CI")

kable(sum_stat)
Context variable meanFac stdFac nObs seMean CI95
English in Germany Factor1 -0.7673653 1.2968739 70 0.1550061 0.3038119
English in Germany Factor2 0.2464654 0.8811501 70 0.1053176 0.2064224
English in Germany Factor3 -0.4025772 0.6874385 70 0.0821646 0.1610427
English in Germany Factor4 0.3070734 0.9509279 70 0.1136576 0.2227689
English in Germany Factor5 0.0672303 0.8805028 70 0.1052402 0.2062708
English in Germany Factor6 -0.3916784 1.0244232 70 0.1224420 0.2399863
English in Italy Factor1 0.1674403 0.7825545 91 0.0820340 0.1607866
English in Italy Factor2 0.5194919 0.7674261 91 0.0804481 0.1576783
English in Italy Factor3 0.0054434 0.9238201 91 0.0968427 0.1898116
English in Italy Factor4 0.0311704 0.9431596 91 0.0988700 0.1937852
English in Italy Factor5 0.4084333 0.8766512 91 0.0918980 0.1801201
English in Italy Factor6 0.3768171 0.8018947 91 0.0840614 0.1647604
German in Australia Factor1 0.2302922 0.7943686 88 0.0846800 0.1659728
German in Australia Factor2 -0.2004232 1.0176085 88 0.1084774 0.2126158
German in Australia Factor3 0.0300804 1.1010277 88 0.1173699 0.2300451
German in Australia Factor4 -0.2897234 1.1273569 88 0.1201766 0.2355462
German in Australia Factor5 -0.3245639 1.0203688 88 0.1087717 0.2131925
German in Australia Factor6 -0.0289222 1.0127781 88 0.1079625 0.2116065
Italian in Australia Factor1 0.2461188 0.7676304 74 0.0892352 0.1749010
Italian in Australia Factor2 -0.6336365 0.9310097 74 0.1082277 0.2121263
Italian in Australia Factor3 0.3383511 1.0930484 74 0.1270643 0.2490460
Italian in Australia Factor4 0.0157300 0.8670613 74 0.1007938 0.1975559
Italian in Australia Factor5 -0.1798909 1.0572445 74 0.1229022 0.2408883
Italian in Australia Factor6 -0.0584826 1.0369273 74 0.1205404 0.2362591

3.3 Factor analysis using 6 factors correcting for context and degree (which will become the final)

3.3.1 Check what is the effect of 0 years vs all in year.studyL2

We can see that L2 o vs >1 does not have an effect on the items apart bordeline for dream.

dat <- dat %>%
  dplyr::mutate(item = fct_reorder(item,X4),.desc = FALSE) %>%
  dplyr::rename(Effect = X3,
                low95CI = X1,
                high95CI = X2,
                pval = X4)
Error in length(f) == length(.x) : object 'X4' not found
  • Effect and 95%CI for dream
dat[dat$item %in% "dream",]

3.4 Try FA correcting also for L2 (0 vs >0) (on top of Context and degree)

rr rr # items to be used for the FA usable_items <- likert_variables1[!(likert_variables1 %in% c(1,1,.comm1, .comm1, .comm1))]

usable_data <- all[,c(usable_items,,,.studyL2)] usable_data\(degree_binary <- ifelse(usable_data\)degree %in% c(.SCI,), , ifelse(usable_data\(degree %in% \LA\,\LA\,\HUM\)) usable_data\)year.studyL2_binary <- ifelse(usable_data$year.studyL2 == years,0,1) dat_onlyItems <- usable_data[,usable_items]

4 get residuals after regressing for context

get_residuals <- function(item,pred1,pred2,pred3){ mod <- lm(item ~ pred1 + pred2 +pred3) return(mod$residuals) }

applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals, pred1=usable_data\(Context,pred2=usable_data\)degree_binary,pred3=usable_data$year.studyL2_binary)

5 Factanal

6 From a statisticak point of view

fap <- fa.parallel(applygetRes) fact <- 6 loading_cutoff <- 0.2 fa_basic <- fa(applygetRes,fact)

fa_basic

7 plot loadings

loadings_basic <- fa_basic\(loadings class(loadings_basic)<-\matrix\ colnames(loadings_basic)<-paste(\F\,1:fact,sep=\\) loadings_basic<-as.data.frame(loadings_basic) loadings_basic<-round(loadings_basic,2) loadings_basic\)D <- rownames(loadings_basic) a1 <- loadings_basic

a2 <- melt(a1,id.vars=c()) a2\(inv <- ifelse(a2\)value < 0 ,,) a2\(value[abs(a2\)value) < loading_cutoff] <- 0 a2 <- a2[a2$value!=0,] a2 <- a2 %>% separate(D,into = c(,),remove=FALSE,sep=[.])

ggplot(a2)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat=)+facet_wrap(~variable,ncol = 2,scales = _y)+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype=,colour=red)

7.1 Factor analysis correcting for context and degree and removing 0 years for year.studyL2

rr rr # items to be used for the FA usable_items <- likert_variables1[!(likert_variables1 %in% c(1,1,.comm1, .comm1, .comm1))]

usable_data <- all[,c(usable_items,,,.studyL2)] dat_onlyItems <- usable_data[,usable_items] dat_onlyItems <- dat_onlyItems[usable_data$year.studyL2 != years,] usable_data <- usable_data[usable_data$year.studyL2 != years,]

8 get residuals after regressing for context

get_residuals <- function(item,pred1,pred2){ mod <- lm(item ~ pred1 + pred2) return(mod$residuals) }

applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals, pred1=usable_data\(Context,pred2=usable_data\)degree)

9 Factanal

10 From a statisticak point of view

fap <- fa.parallel(applygetRes) fact <- 7 loading_cutoff <- 0.2 fa_basic <- fa(applygetRes,fact)

fa_basic

11 plot loadings

loadings_basic <- fa_basic\(loadings class(loadings_basic)<-\matrix\ colnames(loadings_basic)<-paste(\F\,1:fact,sep=\\) loadings_basic<-as.data.frame(loadings_basic) loadings_basic<-round(loadings_basic,2) loadings_basic\)D <- rownames(loadings_basic) a1 <- loadings_basic

a1 <- melt(a1,id.vars=c()) a1\(inv <- ifelse(a1\)value < 0 ,,) a1\(value[abs(a1\)value) < loading_cutoff] <- 0 a1 <- a1[a1$value!=0,] a1 <- a1 %>% separate(D,into = c(,),remove=FALSE,sep=[.])

ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat=)+facet_wrap(~variable,ncol = 2,scales = _y)+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype=,colour=red)

12 Table of the factors

loadings_basic$D <- NULL loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0 for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}

loadings_basic[loadings_basic==] <- \ loading_fact_reduced <- loadings_basic loading_fact_reduced

13 predict values per samples

pred_basic <- as.data.frame(predict(fa_basic,dat_onlyItems)) names(pred_basic) <- paste(,1:fact,sep = \)

factors <- names(pred_basic) match_initial_data <- match(all$Resp.ID,rownames(pred_basic)) all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,])) corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = ))

14 Plot loadings by context

all_complete_melt <- melt(all_complete_basic,id.vars = ,measure.vars = factors)

library(ggplot2) ggplot(all_complete_melt)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)

15 error bar

sum_stat <- all_complete_melt %>% group_by(Context,variable) %>% summarise(meanFac = mean(value,na.rm=TRUE), stdFac = sd(value,na.rm=TRUE), nObs = length(Context[!is.na(value)])) %>% mutate(seMean = stdFac/sqrt(nObs), CI95 = 1.96*seMean)

ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) + geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~variable,scales=_y) + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle(+- 95% CI)

ggplot(sum_stat,aes(x=variable,y=meanFac,colour=variable)) + geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~Context,scales=_y) + geom_point() + ggtitle(+- 95% CI)

kable(sum_stat)

---
title: "Factor analysis"
author: "Anna Quaglieri & Riccardo Amorati"
date: "03/09/2017"
output:
  github_document:
    toc: yes
    toc_depth: 4
  html_document:
    toc: yes
    toc_depth: '4'
  html_notebook:
    code_folding: hide
    fig_caption: yes
    number_sections: yes
    toc: yes
    toc_float: yes
---

```{r,message=FALSE, echo=FALSE}
library(readr)
library(tidyverse)
library(reshape2)
library(corrplot)
library(psych)
library(pheatmap)
library(RColorBrewer)
library(cowplot)
library(gridExtra)
library(cowplot)
library(pheatmap)
library(sjPlot)
library(sjlabelled)
library(sjmisc)
library(knitr)

data(efc)
theme_set(theme_sjplot())

# Chunk options
knitr::opts_chunk$set(echo = TRUE, prompt = TRUE,cache = TRUE,fig.width = 10,fig.height = 8)

```

## Exploratory factor analysis: 7 factors as the number of variables in the study design

## Read in data

```{r}
all <- read.csv("../02-descriptive_data/merged_filtered_imputedMedian_likertNumber.csv")
rownames(all) <- all$Resp.ID
```

Seven, is the number of factors that would be present according to the study design.
Using very relaxed cutoff of 0.2 to get rid of not important variables in each factor.

### Likert variables

```{r include=FALSE,message=FALSE}
likert_grep <- "\\.id$|\\.ought$|\\.intr$|\\.instru$|\\.integr$|\\.prof$|\\.post$|\\.comm$|^necessity$|^educated$"

# all
likert_variables_all <- colnames(all)[grep(likert_grep,colnames(all))]
likert_variables_all
likert_variables_all <- likert_variables_all[!(likert_variables_all %in% "other.prof")]

# likert variables converted to numbers
likert_variables1 <- paste0(likert_variables_all,"1")

```

# Final Factanal correcting for degree and Context and not for L2 

When correcting for context what we are doing is that we are removing the context mean from every context

```{r}
# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]

usable_data <- all[,c(usable_items,"Context","degree")]
usable_data$degree_binary <- ifelse(usable_data$degree %in% c("HUM.SCI","SCI"), "SCI",
                                    ifelse(usable_data$degree %in% "LA","LA","HUM"))
dat_onlyItems <- usable_data[,usable_items]

# get residuals after regressing for context
get_residuals <- function(item,pred1,pred2){
  mod <- lm(item ~ pred1 + pred2)
  return(mod$residuals)
}

applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals,
                     pred1=usable_data$Context,pred2=usable_data$degree_binary)

```

**Compare correlation matrix before and after correcting**

```{r}
before <- cor(as.matrix(dat_onlyItems))
after <- cor(applygetRes)

dif <- before - after
hist(dif)
```


```{r}
########################

### added
# before <- data.frame(dat_onlyItems)
# after <- data.frame(applygetRes)
# 
# cov <- cor(after,method = "pearson",use="pairwise.complete.obs")
# 
# row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
# row_infos$Variables <- as.character(row_infos$Variables)
# rownames(row_infos) <- rownames(cov)
# row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
# row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
# 
# ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
# ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
# 
# pheatmap(cov, main = "All",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
# ,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
# ### added
# 
# after$Context <- usable_data$Context[match(rownames(after),rownames(usable_data))]
# before$Context <- usable_data$Context[match(rownames(before),rownames(usable_data))]
# 
# ggplot(after,aes(x=fail.ought1,fill=Context)) + geom_histogram() + facet_wrap(~Context)
# plot(after$fail.ought1,before$fail.ought1)
# ggplot(before,aes(x=speaking.prof1,fill=Context)) + geom_histogram() + facet_wrap(~Context)
# ggplot(before,aes(x=fail.ought1,fill=Context)) + geom_histogram() + facet_wrap(~Context)


# Factanal 
# From a statisticak point of view 
fap <- fa.parallel(applygetRes)
fact <- 6
loading_cutoff <- 0.2
fa_basic <- fa(applygetRes,fact)

# analyse residuals vs initial
#fa_basic <- fa(applygetRes, fact,scores="regression")
#fac <- as.matrix(dat_onlyItems) %*% loadings(fa_basic,cutoff = 0.3)

# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic

a2 <- melt(a1,id.vars=c("D"))
a2$inv <- ifelse(a2$value < 0 ,"neg","pos")
a2$value[abs(a2$value) < loading_cutoff] <- 0
a2 <- a2[a2$value!=0,]
a2 <- a2 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")

ggplot(a2)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")

# Factors one by one
ggplot(subset(a2,variable %in% "F1"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F1")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F2"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F2") + labs(y="Items")

ggplot(subset(a2,variable %in% "F3"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F3")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F4"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F4")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F5"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F5")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F6"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F6")+ labs(y="Items")
```

## Chronbach alpha

```{r}
f1 <- unique(a2$D[a2$variable %in% "F1"])
f2 <- unique(a2$D[a2$variable %in% "F2"])
f3 <- unique(a2$D[a2$variable %in% "F3"])
f4 <- unique(a2$D[a2$variable %in% "F4"])
f5 <- unique(a2$D[a2$variable %in% "F5"])
f6 <- unique(a2$D[a2$variable %in% "F6"])

psych::alpha(applygetRes[,colnames(applygetRes) %in% f1])
psych::alpha(applygetRes[,colnames(applygetRes) %in% f2])
psych::alpha(applygetRes[,colnames(applygetRes) %in% f3])
psych::alpha(applygetRes[,colnames(applygetRes) %in% f4])
psych::alpha(applygetRes[,colnames(applygetRes) %in% f5])
psych::alpha(applygetRes[,colnames(applygetRes) %in% f6])
```

```{r}
# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}

loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
kable(loading_fact_reduced)

# predict values per samples from initial likert scale
pred_basic <- as.data.frame(predict(fa_basic,dat_onlyItems,applygetRes))
#pred_basic <- as.data.frame(predict(fa_basic,applygetRes))
#https://stackoverflow.com/questions/4145400/how-to-create-factors-from-factanal
#pred_basic <- data.frame(as.matrix(dat_onlyItems) %*% loadings(fa_basic,cutoff=0))
names(pred_basic) <- paste("Factor",1:fact,sep = "")

factors <- names(pred_basic)
all_complete_basic <- data.frame(pred_basic,all[match(all$Resp.ID,rownames(pred_basic)),])
#match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
#all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))

# Plot loadings by context
all_complete_melt <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)

ggplot(all_complete_melt) + geom_boxplot(aes(x=Context,y=value,color=Context)) + facet_wrap(~variable) + coord_flip() + guides(color=F)

# error bar 
sum_stat <- all_complete_melt %>% group_by(Context,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Context[!is.na(value)])) %>%
  mutate(stdMean = stdFac/sqrt(nObs),
         CIspread=1.96*stdMean,
         LowerBoundCI = meanFac - 1.96*stdMean,
         UpperBoundCI = meanFac + 1.96*stdMean)

sum_stat$Context <- factor(sum_stat$Context,levels=c("English in Italy", "English in Germany", "Italian in Australia", "German in Australia"))

ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) + 
geom_errorbar(aes(ymin=LowerBoundCI, ymax=UpperBoundCI),width=0.2) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle("Mean +- 95% CI") 

kable(sum_stat)
```

## Linear models testing the effect of context

```{r}
pred_basic <- data.frame(pred_basic)
fact_data <- data.frame(pred_basic,all[match(all$Resp.ID,rownames(pred_basic)),c("Context","Resp.ID")])
sum(fact_data$Resp.ID != rownames(pred_basic))

summary(lm(Factor1 ~ Context,data=fact_data))
summary(lm(Factor2 ~ Context,data=fact_data))
summary(lm(Factor3 ~ Context,data=fact_data))
summary(lm(Factor4 ~ Context,data=fact_data))
summary(lm(Factor5 ~ Context,data=fact_data))
summary(lm(Factor6 ~ Context,data=fact_data))

summary(aov(Factor1 ~ Context,data=fact_data))
summary(aov(Factor2 ~ Context,data=fact_data))
summary(aov(Factor3 ~ Context,data=fact_data))
summary(aov(Factor4 ~ Context,data=fact_data))
summary(aov(Factor5 ~ Context,data=fact_data))
summary(aov(Factor6 ~ Context,data=fact_data))
```

## All pairwise comparisons

Independent t-test are performed between every pair of contexts within every factor. The Bonferroni correction is used to adjust the p-values for multiple testing.

```{r}
pairwise.t.test.with.t.and.df <- function (x, g, p.adjust.method = p.adjust.methods, pool.sd = !paired, 
                                           paired = FALSE, alternative = c("two.sided", "less", "greater"), 
                                           ...) 
{
    if (paired & pool.sd) 
        stop("pooling of SD is incompatible with paired tests")
    DNAME <- paste(deparse(substitute(x)), "and", deparse(substitute(g)))
    g <- factor(g)
    p.adjust.method <- match.arg(p.adjust.method)
    alternative <- match.arg(alternative)
    if (pool.sd) {
        METHOD <- "t tests with pooled SD"
        xbar <- tapply(x, g, mean, na.rm = TRUE)
        s <- tapply(x, g, sd, na.rm = TRUE)
        n <- tapply(!is.na(x), g, sum)
        degf <- n - 1
        total.degf <- sum(degf)
        pooled.sd <- sqrt(sum(s^2 * degf)/total.degf)
        compare.levels <- function(i, j) {
            dif <- xbar[i] - xbar[j]
            se.dif <- pooled.sd * sqrt(1/n[i] + 1/n[j])
            t.val <- dif/se.dif
            if (alternative == "two.sided") 
                2 * pt(-abs(t.val), total.degf)
            else pt(t.val, total.degf, lower.tail = (alternative == 
                                                         "less"))
        }
        compare.levels.t <- function(i, j) {
            dif <- xbar[i] - xbar[j]
            se.dif <- pooled.sd * sqrt(1/n[i] + 1/n[j])
            t.val = dif/se.dif 
            t.val
        }       
    }
    else {
        METHOD <- if (paired) 
            "paired t tests"
        else "t tests with non-pooled SD"
        compare.levels <- function(i, j) {
            xi <- x[as.integer(g) == i]
            xj <- x[as.integer(g) == j]
            t.test(xi, xj, paired = paired, alternative = alternative, 
                   ...)$p.value
        }
        compare.levels.t <- function(i, j) {
            xi <- x[as.integer(g) == i]
            xj <- x[as.integer(g) == j]
            t.test(xi, xj, paired = paired, alternative = alternative, 
                   ...)$statistic
        }
        compare.levels.df <- function(i, j) {
            xi <- x[as.integer(g) == i]
            xj <- x[as.integer(g) == j]
            t.test(xi, xj, paired = paired, alternative = alternative, 
                   ...)$parameter
        }
    }
    PVAL <- pairwise.table(compare.levels, levels(g), p.adjust.method)
    TVAL <- pairwise.table.t(compare.levels.t, levels(g), p.adjust.method)
    if (pool.sd) 
        DF <- total.degf
    else
        DF <- pairwise.table.t(compare.levels.df, levels(g), p.adjust.method)           
    ans <- list(method = METHOD, data.name = DNAME, p.value = PVAL, 
                p.adjust.method = p.adjust.method, t.value = TVAL, dfs = DF)
    class(ans) <- "pairwise.htest"
    ans
}
pairwise.table.t <- function (compare.levels.t, level.names, p.adjust.method) 
{
    ix <- setNames(seq_along(level.names), level.names)
    pp <- outer(ix[-1L], ix[-length(ix)], function(ivec, jvec) sapply(seq_along(ivec), 
        function(k) {
            i <- ivec[k]
            j <- jvec[k]
            if (i > j)
                compare.levels.t(i, j)               
            else NA
        }))
    pp[lower.tri(pp, TRUE)] <- pp[lower.tri(pp, TRUE)]
    pp
}
```

https://stackoverflow.com/questions/27544438/how-to-get-df-and-t-values-from-pairwise-t-test

```{r}
#f1 <- pairwise.t.test(x = fact_data$Factor1, g = fact_data$Context,p.adjust.method = "none",pool.sd = TRUE)
#kable(f1$p.value,digits = 20)

pair.t.test <- function(x, context,fname = "F1"){
  a <- x[context %in% "English in Germany"]
  b <- x[context %in% "English in Italy"]
  c <- x[context %in% "German in Australia"]
  d <- x[context %in% "Italian in Australia"]
  
  ab <- t.test(a,b,var.equal = TRUE)
  ac <- t.test(a,c,var.equal = TRUE)
  ad <- t.test(a,d,var.equal = TRUE)
  bc <- t.test(b,c,var.equal = TRUE)
  bd <- t.test(b,d,var.equal = TRUE)
  cd <- t.test(c,d,var.equal = TRUE)
  
  test_out <- data.frame(Factor = fname,
                         Context1 = c("English in Germany","English in Germany","English in Germany",
                                      "English in Italy","English in Italy","German in Australia"),
                         Context2 = c("English in Italy","German in Australia","Italian in Australia",
                                      "German in Australia","Italian in Australia","Italian in Australia"),
                         t.value = c(ab$statistic,ac$statistic,ad$statistic,bc$statistic,bd$statistic,cd$statistic),
                         p.value = c(ab$p.value,ac$p.value,ad$p.value,bc$p.value,bd$p.value,cd$p.value),
                         estimate1 = c(ab$estimate[1],ac$estimate[1],ad$estimate[1],bc$estimate[1],bd$estimate[1],cd$estimate[1]),
                         estimate2 = c(ab$estimate[2],ac$estimate[2],ad$estimate[2],bc$estimate[2],bd$estimate[2],cd$estimate[2]),
                         confint1 = c(ab$conf.int[1],ac$conf.int[1],ad$conf.int[1],bc$conf.int[1],bd$conf.int[1],cd$conf.int[1]),
                         confint2 = c(ab$conf.int[2],ac$conf.int[2],ad$conf.int[2],bc$conf.int[2],bd$conf.int[2],cd$conf.int[2]),
                         df = c(ab$parameter,ac$parameter,ad$parameter,bc$parameter,bd$parameter,cd$parameter))
  
  return(test_out)
}

f1 <- pair.t.test(x=fact_data$Factor1,fact_data$Context,fname = "F1")
f2 <- pair.t.test(x=fact_data$Factor2,fact_data$Context,fname = "F2")
f3 <- pair.t.test(x=fact_data$Factor3,fact_data$Context,fname = "F3")
f4 <- pair.t.test(x=fact_data$Factor4,fact_data$Context,fname = "F4")
f5 <- pair.t.test(x=fact_data$Factor5,fact_data$Context,fname = "F5")
f6 <- pair.t.test(x=fact_data$Factor6,fact_data$Context,fname = "F6")

tott <- rbind(f1,f2,f3,f4,f5,f6)
tott$p.adjusted <- p.adjust(tott$p.value,method = "fdr")

kable(tott)
```


```{r}
fact_data1 <- fact_data[,c("Factor1","Context","Resp.ID")] %>% spread(key = Context, value = Factor1,drop=TRUE)
```

# Demographics

Variables have been recoded and we need to do the models.

```{r eval=FALSE}
demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")

# Combine with demo variables
pred_basic <- data.frame(pred_basic)
demo_data <- data.frame(pred_basic,all[match(all$Resp.ID,rownames(pred_basic)),c("Resp.ID",demographics_var)])
sum(demo_data$Resp.ID != rownames(pred_basic))

# Gender
longData <- demo_data %>% gather(key = FactorLabel,value = FactorValue,Factor1:Factor6) %>%
  group_by(Gender,FactorLabel) %>%
  summarise(meanDemo = mean(FactorValue),
            sdDemo =  sd(FactorValue))

summary(lm(Factor1 ~ prof + Gender + Age + Context,data = demo_data))
summary(lm(Factor1 ~ Context:prof,data = demo_data))
summary(lm(Factor2 ~ prof + Context,data = demo_data))
summary(lm(Factor3 ~ prof + Context,data = demo_data))

summary(lm(Factor1 ~ origin + Gender + Age + Context,data = demo_data))
summary(lm(Factor1 ~ Context:prof,data = demo_data))
summary(lm(Factor2 ~ prof + Context,data = demo_data))
summary(lm(Factor3 ~ prof + Context,data = demo_data))


table(dat_fac_demo$L1) # to be changed
dat_fac_demo$L1_expected <- ifelse(as.character(dat_fac_demo$L1) %in% c("German","Italian","English"),"Yes","No")
table(dat_fac_demo$L1_expected)

table(dat_fac_demo$speak.other.L2) # to be changed
dat_fac_demo$speak.other.L2_binary <- ifelse(!is.na(dat_fac_demo$speak.other.L2) & 
                                      !(dat_fac_demo$speak.other.L2 %in% c("Yes","No")),"Yes",as.character(all$speak.other.L2))
table(dat_fac_demo$speak.other.L2_binary)

#table(dat_fac_demo$study.other.L2) # to be changed

table(dat_fac_demo$origins)

table(dat_fac_demo$year.studyL2) # da vedere only for Australian contexts

table(dat_fac_demo$degree) # to be tried- only interesting for Australia
# Merge BA in Anglistik  with BA in Nordamerikastudien
dat_fac_demo$degree1 <- dat_fac_demo$degree
dat_fac_demo$degree1[dat_fac_demo$degree1 %in% "BA in Nordamerikastudien"] <- "BA in Anglistik"

table(dat_fac_demo$prof)
table(dat_fac_demo$L2.VCE)

#table(dat_fac_demo$other5.other.ways) # to be changed
```

```{r}
demo_melt <- melt(all_complete_basic,id.vars = c("Age","Gender","origins","study.year","prof","L2.VCE","Context"),measure.vars = factors)

# age
ageStat <- demo_melt %>% group_by(Context,Age,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Age[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)

ageStat$Demo <- "Age"
colnames(ageStat)[2] <- "levels"
ageStat <- data.frame(ageStat)

# Gender
GenderStat <- demo_melt %>% group_by(Context,Gender,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Gender[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)

GenderStat$Demo <- "Gender"
colnames(GenderStat)[2] <- "levels"
GenderStat <- data.frame(GenderStat)

# origins
originsStat <- demo_melt %>% group_by(Context,origins,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(origins[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)

originsStat$Demo <- "origins"
colnames(originsStat)[2] <- "levels"
originsStat <- data.frame(originsStat)

# study.year
study.yearStat <- demo_melt %>% group_by(Context,study.year,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(study.year[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)

study.yearStat$Demo <- "Study Year"
colnames(study.yearStat)[2] <- "levels"
study.yearStat <- data.frame(study.yearStat)

# prof
profStat <- demo_melt %>% group_by(Context,prof,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(prof[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)

profStat$Demo <- "Proficiency"
colnames(profStat)[2] <- "levels"
profStat$levels <- as.character(profStat$levels)
profStat <- data.frame(profStat)

# L2.VCE
L2.VCEStat <- demo_melt %>% group_by(Context,L2.VCE,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(L2.VCE[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)

L2.VCEStat$Demo <- "L2.VCE"
colnames(L2.VCEStat)[2] <- "levels"
L2.VCEStat$levels <- as.character(L2.VCEStat$levels)
L2.VCEStat <- data.frame(L2.VCEStat)

##################
# Combine stats
##################

combine_stat <- rbind(data.frame(L2.VCEStat),data.frame(profStat),study.yearStat,originsStat,ageStat,GenderStat)
```

### Tables

- **Age**

```{r}
kable(ageStat)
```

- **Gender**

```{r}
kable(GenderStat)
```

- **origins**

```{r}
kable(originsStat)
```

- **study.year**

```{r}
kable(study.yearStat)
```

- **prof**

```{r}
kable(profStat)
```

- **L2.VCE**

```{r}
kable(L2.VCEStat)
```

### Factor means with Confidence Intervals

```{r}
pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor1")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor1: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor2")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor2: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor3")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor3: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor4")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor4: Mean +- 95% CI") + theme_bw()


pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor5")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor5: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor6")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor6: Mean +- 95% CI") + theme_bw()
```

# Other tentatives

## FA with 7 factors (as from design)

```{r}
# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]

usable_data <- all[,usable_items]
sum(is.na(usable_data))

# Cronbach's alpha using consistent items across contexts
psych::alpha(usable_data,use="pairwise.complete.obs")

fact <- 7
loading_cutoff <- 0.2
fa_basic <- fa(usable_data,fact)

fa_basic

# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic

a1 <- melt(a1,id.vars=c("D"))
a1$inv <- ifelse(a1$value < 0 ,"neg","pos")
a1$value[abs(a1$value) < loading_cutoff] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")

ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")

# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}

loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced

# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,usable_data))
names(pred_basic) <- paste("Factor",1:fact,sep = "")

factors <- names(pred_basic)
match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))

# Plot loadings by context
all_complete_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)

library(ggplot2)
ggplot(all_complete_basic)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)
# 7 * 12 rows removed

```

## Basic factor analysis: 6 factors following the fa.parallel suggestion

Using very relaxed cutoff of 0.2 to get rid of not important variables in each factor.

```{r}
# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]

usable_data <- all[,usable_items]

# From a statisticak point of view 
fap <- fa.parallel(usable_data)
fact <- 6
loading_cutoff <- 0.2
fa_basic <- fa(usable_data,fact)

fa_basic

# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic

a1 <- melt(a1,id.vars=c("D"))
a1$inv <- ifelse(a1$value < 0 ,"neg","pos")
a1$value[abs(a1$value) < loading_cutoff] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")

ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")

# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}

loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced

# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,usable_data))
names(pred_basic) <- paste("Factor",1:fact,sep = "")

factors <- names(pred_basic)
match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))

# Plot loadings by context
all_complete_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)

library(ggplot2)
ggplot(all_complete_basic)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)
# 7 * 12 rows removed

# error bar 
sum_stat <- all_complete_basic %>% group_by(Context,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Context[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)

ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle("Mean +- 95% CI")

ggplot(sum_stat,aes(x=variable,y=meanFac,colour=variable)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~Context,scales="free_y") + 
  geom_point() + ggtitle("Mean +- 95% CI")

kable(sum_stat)
```

## Factor analysis using 6 factors correcting for context and degree (which will become the final)

### Check what is the effect of 0 years vs all in year.studyL2

We can see that L2 o vs >1 does not have an effect on the items apart bordeline for dream. 

```{r fig.height=8,fig.width=6}
# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]

usable_data <- all[,c(usable_items,"Context","degree","year.studyL2")]
dat_onlyItems <- usable_data[,usable_items]

# get residuals after regressing for context
get_residuals <- function(item,pred1,pred2,pred3){
  mod <- lm(item ~ pred1 + pred2 + pred3)
  dat <- rbind(confint(mod)[7,1],confint(mod)[7,2],
               summary(mod)$coefficients[7,1],
               pval = summary(mod)$coefficients[7,4])
  return(dat)
}

usable_data$year.studyL2_binary <- ifelse(usable_data$year.studyL2 == "0 years",0,1)
usable_data$degree_binary <- ifelse(usable_data$degree %in% c("HUM.SCI","SCI"), "SCI",
                                    ifelse(usable_data$degree %in% "LA","LA","HUM"))
#mod <- lm(written.prof1 ~ Context + degree_binary + year.studyL2_binary,data=usable_data)
#summary(mod)

applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals,
                     pred1=usable_data$Context,pred2=usable_data$degree_binary,pred3=usable_data$year.studyL2_binary)

dat <- data.frame(t(applygetRes))
dat$item <- rownames(dat)
dat <- dat %>% separate(item,into=c("item","variable"),sep="[.]")

pos <- position_dodge(width=0.4)
dat <- dat %>%
  dplyr::mutate(item = fct_reorder(item,X4),.desc = FALSE) %>%
  dplyr::rename(Effect = X3,
                low95CI = X1,
                high95CI = X2,
                pval = X4)
ggplot(dat,aes(x=item,y=Effect,colour=variable)) + 
geom_errorbar(aes(ymin=low95CI, ymax=high95CI),width=0.2,position=pos) +  geom_point(position=pos) + ggtitle("0 years of L2 vs >0 years L2 Effect") + theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1))+ geom_hline(yintercept = 0,linetype="dotted",colour="dark red",size=1)+ ylab("Effect +- 95% CI\nOrdered by p-value") + coord_flip()
```

* Effect and 95%CI for `dream`

```{r}
dat[dat$item %in% "dream",]
```




## Try FA correcting also for L2 (0 vs >0) (on top of Context and degree)

```{r}
# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]

usable_data <- all[,c(usable_items,"Context","degree","year.studyL2")]
usable_data$degree_binary <- ifelse(usable_data$degree %in% c("HUM.SCI","SCI"), "SCI",
                                    ifelse(usable_data$degree %in% "LA","LA","HUM"))
usable_data$year.studyL2_binary <- ifelse(usable_data$year.studyL2 == "0 years",0,1)
dat_onlyItems <- usable_data[,usable_items]


# get residuals after regressing for context
get_residuals <- function(item,pred1,pred2,pred3){
  mod <- lm(item ~ pred1 + pred2 +pred3)
  return(mod$residuals)
}

applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals,
                     pred1=usable_data$Context,pred2=usable_data$degree_binary,pred3=usable_data$year.studyL2_binary)

# Factanal 
# From a statisticak point of view 
fap <- fa.parallel(applygetRes)
fact <- 6
loading_cutoff <- 0.2
fa_basic <- fa(applygetRes,fact)

fa_basic

# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic

a2 <- melt(a1,id.vars=c("D"))
a2$inv <- ifelse(a2$value < 0 ,"neg","pos")
a2$value[abs(a2$value) < loading_cutoff] <- 0
a2 <- a2[a2$value!=0,]
a2 <- a2 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")

ggplot(a2)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")
```


## Factor analysis correcting for context and degree and removing 0 years for year.studyL2

```{r}
# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]

usable_data <- all[,c(usable_items,"Context","degree","year.studyL2")]
dat_onlyItems <- usable_data[,usable_items]
dat_onlyItems <- dat_onlyItems[usable_data$year.studyL2 != "0 years",]
usable_data <- usable_data[usable_data$year.studyL2 != "0 years",]


# get residuals after regressing for context
get_residuals <- function(item,pred1,pred2){
  mod <- lm(item ~ pred1 + pred2)
  return(mod$residuals)
}

applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals,
                     pred1=usable_data$Context,pred2=usable_data$degree)

# Factanal 
# From a statisticak point of view 
fap <- fa.parallel(applygetRes)
fact <- 7
loading_cutoff <- 0.2
fa_basic <- fa(applygetRes,fact)

fa_basic

# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic

a1 <- melt(a1,id.vars=c("D"))
a1$inv <- ifelse(a1$value < 0 ,"neg","pos")
a1$value[abs(a1$value) < loading_cutoff] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")

ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")

# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}

loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced

# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,dat_onlyItems))
names(pred_basic) <- paste("Factor",1:fact,sep = "")

factors <- names(pred_basic)
match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))

# Plot loadings by context
all_complete_melt <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)

library(ggplot2)
ggplot(all_complete_melt)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)

# error bar 
sum_stat <- all_complete_melt %>% group_by(Context,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Context[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)

ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle("Mean +- 95% CI")

ggplot(sum_stat,aes(x=variable,y=meanFac,colour=variable)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~Context,scales="free_y") + 
  geom_point() + ggtitle("Mean +- 95% CI")

kable(sum_stat)
```




